Can Games teach us?

Reflections on the optimistic paradigm explained in “Video Games and the Future of Learning”

Video Games are undoubtedly the most pervasive products for entertainment gifted by Computer Science. There has always been a lot of criticism around the effects of games in our lives and our society. Though these digital games have been in existence for quite a long period of time, we still find ourselves in a non-converging decision as to whether they are doing good or bad to us. This is mainly due to the inherent versatility in the kinds and genres of the games and individualistic preferences of the Game Development communities. However, unofficially we often find a vast majority of people who believes that Games are nothing but a sheer waste of time and they leave players in their fantasy worlds which subsequently has bad effects on the personality of the player. In such a belief system of our society, it’s very difficult to make people understand the possible good effects a game can have on a person, if not impossible. In the literature “Video Games and the Future of Learning” written back in 2005 at Wisconsin Center for Education Research, the authors have done an excellent work of explaining the bigger picture of the usefulness of the Game Based environments which can be put for a betterment of the society. He shows various examples of games which feature the situated learning environments and explains how those can affect the overall learning experience of the player. Though there’s no consensus on the good or bad effects of games, almost every researcher and literature believes that a game always makes the player learn “something”. He doesn’t provide any comparison on the good or bad consequences of that “something”, rather explains how we can take advantage of the good side of it and model it further to meet our needs. The literature doesn’t talk much about the side effects of the frameworks explained in it and we believe that it’s the way the authors want to portray their work. His vision of deploying the Game Based Learning environments in every education system may seem a little ambitious at times, but overall his goals were pretty concrete and focused considering it was written 10 years back. After such a long time now after that work was published, we have actually started to see his dreams coming true in some of our educational systems, if not all, which is a great achievement for us. Lots of middle schools in the country now has started deploying ITS(Intelligent Tutoring Systems) in their classrooms and results of student learning are also pretty good. In this paper, we discuss the insights of the frameworks presented in the aforementioned literature and provide our views on the same wherever we feel the need. Towards the end, we also briefly mention few things that we believe the author should have included in his work to make it little more comprehensive. Overall, we must say that the paper is a great reflection on how games should be perceived as a holy agent of education and not always a demon to us.

Why video games can be so effective in learning?:

A very convincing and widely agreed answer to the above question is the presence of virtual reality in these games. As stated by the author, video games let the players be someone which he is not in the real world. It’s a natural human tendency to do stuff which you don’t get to do in your daily lives. So it becomes a big advantage of such systems and they leave a huge imprint on the players mind. These players often get so much immersed in these virtual environments that they start following the values and rules of that simulated environment and start living that virtual identity. If we can mould this advantage to make the players learn good things, then it can have a large impact on the overall learning experience of the players. He talks mainly about “Situated Environment” based games here, which are the ones where the player performs a role of some identity in the virtual world of the game and are often assigned tasks to complete. Few examples included are Lineage, Deus Ex etc. Some games like Clash of Clans, which is classified under the genre of Massively Multiplayer Online Games, are so addictive in nature that players starts to live in that virtual world. This is a very serious instrument which must me modeled to put into a good cause, and a major portion of the paper deals with it. He shows examples of how “knowing and doing” is related to game based environments and backs that up with various suitable examples.

Learning in a Virtual Community:

The notion of how we think or epistemology is one of the key concepts which the paper centers around. In authors words, “It’s a practice which determines how someone in the community decides what questions are worth answering, how to go about answering them, and how to decide when an answer is sufficient”.  Usually learning by doing things among people who already follow the rules and values of what we intend to learn, is relatively much easier than just going through a session about the same. We can leverage this up to a great extent by designing game based environments which has a society with shared values and beliefs related to the intended knowledge domain. The author explains the effectiveness of the approach by citing examples from a game called “Full Spectrum Warrior” which is a video game based on a U.S. Army training simulation. In the game, the player is exposed to a military environment where he has to lead and instruct soldiers to win goals of the game. It focusses on teaching the values as a shared experience. Some part of the intended knowledge domain are instilled into the other characters in the game and some part of the knowledge has to explored by the player himself. This way the learning proceeds in a collaborative manner and is often found very helpful. 

A large portion of the literature explore the ways of using these Epistemic Games for initiation and transformation of the relevant epistemic frames. Examples are drawn from a game named Madison 2200, in which players take the role of an urban planner and solve planning problems in the simulated environments. Results and feedbacks showed a great deal of achievements obtained in terms of learning benefits and players actually started to understand urban panning issue in real life. These show how effectively game based environments can expose even a novice to a learning arena. Similar to these games, we do have few games which help in transforming the views and the ways the experts of that field think. He concludes this section with some pros and cons of both the paradigms.

Games in Schools: Will they allow?

Game Based simulated learning environments have been widely used for training purposes in private and government organizations. US Army is probably one of the major contributors and believers in the field. But we don’t see many invasions of these systems into the educational systems in schools and colleges. The author cites the great potential that these games have in tutoring the students. He gives various examples like he says for Madison 2200, which shows the pedagogical potential of bringing students the experience of being city planners. But this big dream of deploying learning games into our schooling system has a major hurdle to handle with. No matter how effective and an engaging game we produce to be used in schools, we always have a problem with parents and teaching community not encouraging its usage due to the established negative opinions about the games, as mentioned earlier. Even if we make a game which doesn’t teach any violence or bad things, still games inherently expose students to an uncontrolled learning environment which is against most schooling system on today’s date. So before our dreams come true, first we have to focus on changing the believes about the games and influential people speaking about those, is definitely a good start according to the author. 

Concluding Remarks:

If we look back to the whole century about how much advancement we have achieved in our education systems, we find it’s significantly high. We have almost completely digitized the whole system and made the facilities available at a fingertip. But the author states that still somewhere we feel the need to go beyond the classroom programs to make the students take knowledge to a different level. This is where maybe game based intelligent environments can shed some lights. If properly designed, these games can do wonders and help students not only learn things, but also to get attached to “particular social practices”. The work also mentions about some organizations who is favoring intelligent games design in recent years. Finally he discusses some points related to the challenges that we might face while designing such games. Understanding the design of proper virtual worlds which fits students’ needs being the most important thing to follow up. He concludes by stating his dream of how we can march towards a smarter planet by making these intelligent games ubiquitous.

The work in the literature is a unique one in it’s field and truly a motivational and thoughtful read which gave the flavor of the limitless possibilities of Intelligent Game Based Learning environments. However, as we know that the paradigm of gaming itself has a ton of disadvantages along with the pros, we must not forget those as well. We feel that the author could have considered mentioning few things that can go wrong when we try to deploy such systems in huge scale, which can become a major concern at that time. He being completely optimistic about it, have almost left out the dark side of it. We are not suggesting for a pessimistic review here, but just brief touch on some side effects would be a valuable addition. Finally, we also feel that the description could provide some possible insights into the intelligence to be associated with these systems like student modeling and tutor modeling, so that the descriptions provided can be mapped to real to systems easily. Nevertheless, considering all the nice descriptions and insights in the paper and keeping in mind that they wrote this 10 years back from now, we must say that undoubtedly it’s a great literature and is a must read for anyone working in the field.

NB : The article is a critique on the paper Gee, J., Shaffer, D. W., Squire, K. R., & Halverson, R. (2005). Video Games and the future of Learning. Phi Delta Kappan, 67(02).

Pedagogical Agents for Intelligent Tutoring 

Review of “The Effects of a Pedagogical Agent for Informal Science

Education on Learner Behaviors and Self-efficacy”

One to one learning with a teacher has always been one of the most effective paradigms of tutoring in the context of formal education. The efficiency of the learning process increases drastically when we incorporate guides in the process who is well equipped with the capabilities of driving the students through the learning experience, as it has never been so exciting to get knowledge all by ourselves. Like it’s effectiveness in the formal education domain, it has also been found very helpful for the domain of informal education. Informal Education is the term that we use to denote the learning experiences outside the institutions like schools and colleges. Typically, the learning that occurs in some public events, museums, quiz competitions etc. falls under this category. Due the effectiveness they showed in manual instruction systems, these techniques of learning have also been used widely in Intelligent Tutoring Systems(ITS), which are the computer aided artificially intelligent tutoring programs. In the paper mentioned above, the authors have inspected the effect of artificial pedagogical teaching agents in the environment of ITS platforms. It’s a verbose abstract view kind of literature which gives us the rough overview of the experiments they performed and some results associated with it. They also provided a brief analysis of the pros and cons of the results they found without going into much details about the actual ways of how the experimentation took place. I found the paper very introductory at times as it is shallow in nature for the most of the portion and is only effective to have a very rough idea about how this type of pedagogical characters can be incorporated in big tutoring systems. In addition to reading this paper, a further research into the other relevant literature is very much recommended for anyone who is wishing to dive into the world of artificial pedagogical agents to have some more clear view about it. At a glance, they introduced a new agent called Mike and performed experiments with and without his presence in the tutoring systems of a science museum. In another experiment, they introduced two different versions of Mike with different emotional characteristics and studied the efficacy of enthusiasm and self-regulatory feedback for knowledge acquisition. Despite the slight shallowness of the read, I believe that the work described in the paper is really a great reflection of how useful these agents can be, even without many-sophisticated abilities in those environments and the authors very clearly portrayed that in the literature. In a nutshell, it’s definitely a good start for anyone interested in pedagogical agents for ITS platforms. Here we provide the summary of different studies they conducted and our thoughts on the same so that we can see the bigger picture at a glance.

Pedagogical Agents in society:

The design of efficient pedagogical agents has always been an area of active research for educational technologists. Due to the social nature of the learning process, it can take advantage of the features of these agents like nonverbal behaviors, displays of empathy etc. Though there have always been signals showing that these agents help enhance the learning experience, we still not clear about whether the results show up due to the internal properties or the external properties. Apart from the cognitive outcomes, researchers are very much interested in the non-cognitives outcomes of these agents like promotion of  skills such as perseverance, self-control, grit, motivation, and others have long-term societal benefits. We can see the need of these non-cognitive aspects of the learning process is more in case of informal education as compared to it’s counterpart. In case of institutional education, the students are somewhat bound to the learning environments and are often forced to learn a certain way that the system dictates. This, however is not the case with informal education. Imagine a situation where you are trying to explain something about your product to a customer. In this case, the most important thing is to keep the customer engaged in the explanation, after which you can start putting efforts to instill the knowledge components of the explanation. Pedagogical agents in the informal learning systems face the exact same challenges as the salesman in the above example does. This explains the need of non-cognitive tutoring abilities in these agents properly. In the context of the museum environment explained by the authors, they stated that “They must take seriously goals such as convincing a visitor to engage, promoting curiosity and interest, and ensuring that a visitor has a positive learning experience. “ Then they give several examples of successful pedagogical agents deployed in some commercial systems and moves on to the actual description of the agent under research for this publication, “Mike”.

Incorporating Agent in Museum:

In the Cahner’s Computer Place of Museum of Science, Boston, they installed a robot programming interface with a character called Robot Park which had few programmable moves and actions which the visitors could try on through programming the robot. The basic idea was to show the general public how easy can robot programming be and that it is not a rocket science. But often they saw that most of the times the visitors didn’t voluntarily approach to learn the stuffs related to it or sometimes got stuck in between the programming task. In those scenarios, initially, the museum stuff used to help people out wherever they faced difficulties. In 2010, the authorities decided to incorporate a pedagogical agent in the system to help the visitors through the process which eventually can take up the responsibility of the stuff members explaining the moves to the visitors. The agent in the discussion, called “Mike” was primarily designed as a cartoon character to attract the middle school students. The character had a vision of teaching elementary programming concepts through the environment of Robot Park and it had a great collection of abilities from the point of view of both cognitive and non-cognitive aspects of the tutoring cycle. The paper provides snapshots of the character and explain some of it’s abilities with the help of some dialogs extracted from actual usage of the system. The best thing about Mike was it’s engaging feedback system which really helped in the retention of visitors in the programming task. The description helps the reader get a good idea about the usage and usefulness of incorporating the agent in the system.

The two experiments: 

The authors explained the different tests done on the environment where Mike was installed. Basically, there were two sets of experiments. The first one included testing whether incorporating Mike into the Robot Park system was actually helpful. They found that the average holding time of the visitors increased by 51 seconds when Mike was active with Robot Park compared to the situation without Mike. Though they didn’t find any significant improvement in the overall programming behavior in terms of no. of programs written, they could see some improvement in some other aspects like retention time of visitors, probability to attempt and complete the task etc. Also, there was no improvement in visitor ratings while incorporating Mike. The second set of experiments were done to find out the effects of introducing personality and enthusiasm in the agent Mike. They developed two versions of Mike, one is enthusiastic Mike and the other being serious Mike. The former one had lots of appreciating and motivating characteristics in it like applauding the visitors on their successful programming on a task of Robot Park and some other emotional features. The latter one had more of a formal kind of feedback mechanism with very minimalistic reaction sequences. However, they didn’t see much improvement in challenge attempts, programs written or visitor retention. The authors described some other related experiments with the second set of experiments towards the end which explains a little bit of a detailed explanation of the self-efficacy beliefs. Though the second set of experiments doesn’t seem to yield many important results, they still write towards the end that they did detect a modest, but significant increase in visitors’ self-reported self-efficacy ratings when Coach Mike was configured to be enthusiastic and to deliver self-regulatory feedback. Overall, the results of these two sets of experiments showed some really interesting outcomes which indeed bolstered the belief that incorporating a pedagogical agent in an informal learning system is something that we must concentrate on.

Final Remarks:

Making people learn in informal settings has always been a great challenge to mankind. The main problem with these kind of teaching activities outside the class is that the teacher has to act both as a salesman and an instructor to the audience. In such cases, if intelligent tutoring systems can help us resolve this issue with pedagogical agents then the lives of the people who care about it will become much smoother. The architecture and the experiments explained in the paper indeed provides us with some really interesting empirical results based on which many subsequent research activities took place in this domain. The work is explained in a neat and simple manner which targets a vast range of audiences from the novice to the experts. Though it lacks technical details at places, still we believe the literature is certainly fulfilling whatever it was intended to. Along with the nice results obtained from the experiments, the authors also very humbly describes some of the main shortcomings of the research out of which two are really significant ones. First being the lack of experiments which could have checked the need of an animated character for Mike, as all the experiments happened only with the animated Mike and none with it’s non-animated counterpart. Secondly, the author says that he wishes if there were some more user emotion detection capabilities in Mike so that the feedbacks given to the audience were much more tailored to the need of the learning curve. However, considering all the aspects of the work, we would finally like to say that the paper is one of the notable ones in it’s domain and is highly recommended for everybody working in this field.

NB: The above article is a critique on the paper Lane, H. C., Cahill, C., Foutz, S., Auerbach, D., Noren, D., Lussenhop, C., & Swartout, W. (2013, July). The effects of a pedagogical agent for informal science education on learner behaviors and self-efficacy. In Artificial intelligence in education (pp. 309-318). Springer Berlin Heidelberg.

The Emerging Paradigm of Intelligent Negotiations 

Reflections on “BiLAT: A Game-Based Environment for Practicing Negotiation in a Cultural Context”

“Let us move from the era of confrontation to the era of negotiation”, as said  by Richard Milhous Nixon, the 37th President of the United States, gives us a brief idea about the importance of the art of negotiation in the era we are living in. Whether it’s a matter of professional life or personal life, a negotiation can always bring solutions without having to enter a contentious state. Apart from individual perspectives, it plays a very important role in national decisions as well. One of the very prominent areas of usage is our defense system. Military units of different countries are frequently found to be using their negotiation skills at various places to avoid conflicts wherever they can to minimize the harm caused by a battle. So, it often becomes crucial to train the soldiers or other leaders to have equipped with some of the best negotiation skills and hence the defense research is putting a huge effort and investment on the same. Intelligent Tutoring Systems(ITS) are smart automated tutors used to train people in various domains with the help of embedded Artificial Intelligence. One such system Enhanced Learning Environments with Creative Technologies (ELECT) Bi-Lateral Negotiation (BiLAT) is a prototype developed by The USC Institute for Creative Technologies (ICT), in collaboration with organizations within the U.S. Army to address the problem of negotiation skill training through the use of automated learning. In the aforementioned paper in the subtitle, the authors give a detailed overview of the basics of negotiation and the abstract overview of the BiLAT system and what its components are. The domain of negotiation from the point of view of Artificial Intelligence is very ill-defined which causes the designers of such ITS to handle many complexities associated with it. The description of the internal dynamics of negotiations in the paper are very comprehensive and gives the reader a very good knowledge about the domain. However, the details of the BiLAT system is completely verbose and doesn’t include any implementation level details which some readers like me may find little fuzzy if they crave to know more about “how” the different stages or modules were actually put into existence and it’s relevant technologies. The paper is concluded with a comprehensive description of the experiments and the results and we believe that it bolsters the work from a strong mathematical standpoint.

Breaking down the domain of Negotiations:

As mentioned earlier, the ill-defined domain of negotiations pose some serious limitations while implementing intelligent systems for tutoring these skills. Lack of a definition of a proper feedback and the hazy notion of the correctness of moves makes it even more difficult to evaluate the students. Most of the negotiation training typically take place as a role-playing activity and BiLAT system also uses a similar strategy. While building the system, they first tried to find out the actual forces that drive a good negotiation and tired formalizing it as much as it could be done. Previous literature reviews and survey with the military negotiation experts were one of the main inputs to the knowledge base. Finally, they had converged to two facets of the results as stated by the authors, (1) principles that were important for exercising good judgment and decision-making, and (2) procedural steps that supported the principles and increased the chances of success. They categorized the processes used by the experts in three broad phases namely preparation prior to a negotiation, negotiation, and follow-up after a negotiation. The process was further broken down into 12 more stages, the details of which will be omitted here due to space constraints. Also, as the BiLAT game was primarily focussed on military training and loads of those took place in the Middle East area, a huge attention was given on the cultural adaptability aspects. Finally, the formalized stages were converted to some well-defined Learning Objectives(LO) which the tutoring module used for accurate feedback and proper guidance.

The Student’s Perspective and Behavior Models:

During the execution, the central character, i.e. the student(or the player) enters in negotiation meetings with different people in the virtual environment and chooses different actions to perform based on which it progresses through the storyline. The authors have very well described all the stages of the negotiation process and how it looks from the point of view of the player with the help of actual examples of conversations and screenshots from the BiLAT gameplay. The student prepares for the meeting by collecting information on the counterpart and filling up some sheets. During the meeting, the user can select different actions from the GUI of the negotiation environment based on his decisions on the next moves. Depending on his moves, the different matrices of the scene keeps changing, for example the trust meter. The player can also take part in the followup activities and also check if the counterpart followed through anything. There are two main submodules which control the overall behavior of the character while the negotiation is in progress. The first one is called the dialog model which controls the dialog dynamics in the scenario and analyzing down their syntax and semantics. The dialog model is implemented as a rule-based state machine, where the player states on different variables like trust and influence are constantly updated based on player actions. The authors described the details of the model from the point of view of Middle Eastern negotiation scenarios which is very relevant to BiLAT. The reader gets a clear idea how the concepts of the model map to the gameplay mechanics of BiLAT as they describe the steps with real gameplay example screenshots. Unlike the dialog modeling, the negation model used in BiLAT was a previously implemented negotiation architecture called PsychSim, which can be modeled as a social simulation tool. The designers of BiLAT mapped the problem of negotiation to the existing architecture of PsychSim. It describes the characters as entities and their corresponding feature space to create a meaningful behavioral model. In author’s words, PsychSim generates the behavior for these entities and provides explanations of the result in terms of each entity’s goals and beliefs. In a nutshell, the player interacts with the autonomous agents of PsychSim that has its own goals and various negotiation moves. Both dialog model and the negotiation model of the BiLAT system helped the designers to take advantage of the well-defined structures of the negotiation domain while compromising very little on the realistic aspects of it.

How BiLAT does it:

The system is implemented to simulate a virtual environment in which the player has to make certain decisions and move towards the discussion with the counterpart. The various player actions instigate the system to provide feedbacks to the player in terms of the changes in the various player parameters like trust. Learning Objectives(LO) define the modular goals for the tutoring system which define the things the ITS wants to deliver the player as taking home messages. The Coaching aspect of BiLAT tries to categorize the player actions as correct, incorrect or mixed depending on the appropriateness of the actions corresponding to a particular counterpart, scenario or learning objectives for that particular phase. For example, if a player directly rushes down to main business in the opening talk itself, then the coaching module marks it as incorrect as it violates the LO of patience. However, the decisions of when to deliver the coaching messages depend on the actual play sequence of the player and the state of the environment and is decided dynamically. The final important module of BiLAT is the reflective tutoring system which strives to implement the after action review(AAR) phase of the structured negotiation model described above. After the gameplay is over, the players are presented with a detailed feedback and analysis of their actions and the reflective tutoring module tries to point out the mistakes made by the player during the last play. This helps in effectively increasing the player’s knowledge on the LOs. It conducts the AAR sessions based on tutoring tactics and the natural language generation knowledge base. While discussing the actions performed, the students are also provided with the actual gameplay session’s videos to help them remind how they made the choices.

Evaluation Matrices and concluding remarks:

The lack of a definite notion of correctness makes it very hard for the designers to evaluate such systems. Instead of formalizing the evolution like other machine learning tasks, a set of Situational Judgment Tests(SJT) are administered to the students before and after exposure to the tutoring system. These SJTs are set of some case-study evaluation questions from which a user’s pattern of judgement is extracted. Then those patterns are then compared to the patterns of the domain experts in negotiations and found out how aligned the student’s pattern is to theirs. The authors describe the scenarios prepared by The U.S. Army Research Institute and ICT for bilateral negotiation in an Iraqi cultural context with examples from actual tests and corroborated with results. Though the researchers noticed significant increase in the SJT scores after these experiments, they couldn’t conclude that the improvements are solely because of the BiLAT system’s tutoring efficacy. Because the increase in judgmental ability could very well be because of the overall experience of the student during the gameplay. They show a comparison of the results depending on whether participants were commissioned or noncommissioned, and whether participants had prior negotiation experience in a non-western culture or not. Apart from SJT scores, they also collected some gameplay logs from which they modeled some no. of objectives related performance matrices. The author concludes the evaluation discussions saying that none of the above matrices were adequate enough to capture the actual performance of the models. Finally, they give some very insightful future direction on the BiLAT system, among which  the advanced use of NLP techniques for student modeling is a very interesting one. Overall, we believe that the paper is a very effective read and helps the novice learners of Artificial Negotiation domain to understand the basics of the field.

NB: The article is a critique on Kim, J. M., Hill Jr, R. W., Durlach, P. J., Lane, H. C., Forbell, E., Core, M., ... & Hart, J. (2009). BiLAT: A game-based environment for practicing negotiation in a cultural context. International Journal of Artificial Intelligence in Education, 19(3), 289-308.

A floating note to my dear

I miss you.

I keep thinking about new names what I can call you the next time. Because sweetie and honey are too mainstream, you deserve better, something new and sweeter. I keep thinking about the next song I can sing for you. Turns out that, apparently I keep thinking only about you most of the time in my head, staring at your photographs.

It's been so many years since I first saw you and amazingly you still take my breath away every single time I look back into your eyes. That divine smile on your face makes me wanna die for you. I want to do and keep doing all the nicest things on earth for you.

My life is wavering with the tides of destiny. Nothing is going right. I know I am a crap but the only reason I never lose hope is you. You my biggest strength, you my sweetest inspiration. Don't know whether I deserve someone like you or not, I can just thank you from my heart for being in my life. Wanna apologize for the times I hurt you. You have the purest heart I have ever seen. I know you would forgive me.

My day starts with a peek into my phone screen hoping for your texts. If I see one, that gives an eternal ecstasy which I can't even afford to describe in words. Even after being with you for so long, I still smile like those newly "got-into-a-relationship" kinda guys who keeps thinking of being sweet to their loves. You may go on saying I am crazy, I am paranoid or I am over-serious, but I can't help it anymore as you have become the sweetest addiction in my life and I just can't stop caring for you. If you feel the same way as I feel, you would know how hard it is to stay away for so long and how I have managed to stay this way. At the end of the day, no matter whatever happens in life, I just want to be with you soon. And you know what? ... That soon is like, now!

Role of Drama Management in Interactive Narrative 

A review of “Declarative Optimization-Based Drama Management in Interactive Fiction”

Interactive Storytelling or Interact Narrative is one of the digital entertainment products of Artificial Intelligence which has been in practice for quite a long period of time(almost a 40-year history, as quoted in Wikipedia) though it has not been very famous as the other facets of AI till now. To briefly introduce, it’s a genre of intelligent games in which the player interacts with the system by writing in his moves and the outputs and the scene is also shown to him as a description of what’s happening. Due to it’s inherent complexity in understanding human language and typical style of having more than one definite ending of the story, it becomes increasingly complex to design such games as the size of the story explodes. Most of the earlier research in this field greatly failed to deliver on it’s promises and most of them got abandoned in the swirling waves of technology. However, a notable amount of research has been devoted to this field and with the new emerging NLP techniques like Deep Convolutional Neural Networks, we can now look forward to this field in a new direction and with a higher hope. In the aforementioned work of Mark J. Nelson and his co-authors, they have explored some of the aspects of Interactive Narrative related to it’s storyline controlling and evaluation testbeds. One of the most important aspects of such system is controlling the interactions between the system and the player and suggest corresponding storyline branching as accurately and efficiently as possible. Traditionally, this is done using condition-action rules and local triggers which are very inefficient when we scale up the storyline to a larger extent. It also has difficulties capturing the bigger pictures relating to the full control over the gameplay dynamics. In their paper, they explored the concepts of a special approach for storyline controlling called Drama Management(DM), which helps the system to adapt to the player’s moves by changing and reconfiguring the world as the story progresses. This drama management is formally encapsulated under the bigger umbrella of Declarative optimization-based drama management(DODM) which according to him guides the player by projecting possible future stories and reconfiguring the story world based on those projections. One subset of DODM is search-based drama management(SBDM), which is kind of the principle focus of his paper. The authors very carefully and comprehensively describe almost all of the aspects of SBDM in Interactive Storytelling and also provides an elaborate test bed for evaluation of such implementations. Finally, he also shows some interesting shortcomings he discovered for the SBDM paradigm and tried solving this using some Reinforcement Learning approaches, which gives a very useful and strong platform for future research as we are slowly moving towards Reinforcement Learning approaches in many of the AI related domains.

Approaches and the game under experiment: 

The principle idea behind DM is very similar to almost all kinds of Machine Learning models, i.e. to modify the world of the story according to player moves which maximize a particular evaluation function. The authors argue that this typical constraint bases optimization paradigm is very effective for the problem domain under consideration. SBDM, which is a special approach for DODM as already mentioned, uses a search based optimization approach using a traditional game tree search variant. 

The game this paper explores is a horror fiction piece named “Anchorhead” authored by Michael S. Gentry. The original game is quite large and spans over a gameplay of almost 5 days which the researchers under discussion modified to suit their own needs for initial experimentation. In a nutshell, the final modified game had two main subplots, each leading to a different ending based on player actions. We refrain from providing any more details about the game due to space constraints here. As a connection to previous works, a scientist named Weyhrauch did some initial experimentations on all the aspects of DODM in general in a game called “Tea for Three”. Matias and Stern used beat based Drama manager in their game “Faaçde”. However, according to the authors of our paper, all the previous works were done on games of relatively lower scale than “Acnhorhead” and they argue that to what extent previous results could be generalized or scaled was not clear. We feel that the authors pretty much proved their arguments in their work as they very successfully formalized their notions throughout the paper which we will see in the next sections.

Authorial tasks of Drama Management:

The main authorial task of DM is to discretize the continuous story stream of the narrative and project it into an abstract space of something called “plot points” which is the basic parameter on which all the optimizations take place. The authors describe how they had adapted the previous architecture of using directed acyclic graphs introduced by Weyhrauch and extended it further. They very cleverly describe the AND-OR planning graph architecture for the same with relevant examples and drilled into the details of the granularities of the plot points mapping and its effects. Apart from deciding on the level of details, they also talked about maintaining the model of the player and how it can help in providing more efficient hints and storyline control for the DM.

Once we decide on the plot points and the corresponding graphs, next thing we have to do is specifying the different actions the Drama manager can take on. These are the modifications the DM does at runtime to control the execution sequence of the story. We see it as one of the most crucial aspects of DM as they control the main gameplay mechanics of the Game. Authors talked about mainly 5 kinds of DM actions namely permanent deniers, temporary deniers, causers, hints and game endings. All these different set of actions are very well described in the paper with relevant examples from “Anchorhead” and gives a strong foundation for the upcoming discussions. We feel, anyone who is working on DM must have a very clear understanding of these actions and the authors did a great job by introducing them in a fine tuned and concise manner. They also describe some issues they ran into while using DM actions in Anchorhead and how they tackled those. One example which we feel worth mentioning here is the introduction of constraints like “must-follow” and “must-follow-location” in DM actions which helped them overcome the location dependency issues due to bigger size of Anchorhead as compared to earlier experimental games like Tea for Three. By this point of the paper, the readers get a very clear overview of a DM and the authors carefully proceed to the next sections where he talks about the evaluation functions. These functions are defined at each plot point of the story and those are used by DM to decide on the best course of action during the gameplay. This approach is often more effective in action as compared to the author manually specifying all possibilities of the gameplay in advance. The runtime scoring and decisions taken on the basis of those scores can project the decision making in a higher dimensional and complex space which a human can never foresee while writing the story. The authors conclude the sections with some very brief introduction to the general properties of the stories and issues related to multiple endings. At this point, we felt that the description of multiple ending related aspects was little incomplete and leave lots of potential for more explanations. Driving the game climax is one of the most crucial functionalities these systems must handle, so a little more elaborate description would have been gold. Finally, he wraps up with impact of choices and manipulatively in DM.

Experiments and Final thoughts:

The game tree search used by SBDM is essentially similar to traditional Minimax implementation with a small difference that here the player doesn’t act as an absolute adversary to the DM, rather it is more of a random in nature. The author describes two different approaches of evaluation which Weyhrauch used for evaluation DM on Tea for Three and shows the comparisons and other aspects connecting to Anchorhead. The first approach was to search the whole tree with a memoized algorithm which was easy in the case of Tea of Three as it was a relatively small game. But for Anchorhead, it was almost impossible due to it’s much bigger search space. The second method deployed was called Sampling Adversarial Search(SAS) which performs a search on a smaller version of the main game tree. Overall they saw that the results for Anchorhead were not very much promising. In a particular scenario, he even observed that incorporating DM was giving a worse result as compared to no DM. This was primarily dependent on the course of the story and some storyline paths being dominated by a particular kind of DM actions which contributed to these results. Apart from these, they also tried some reinforcement learning approaches which relies on offline computational power rather than banking everything on runtime calculations. The used something called Temporal Difference Learning(TD) which the authors have very vaguely described and if anyone is interested in knowing more about this approach, they must dig into the other relevant literature of the same. They also show the similarities and differences between the search based methods and reinforcement learning methods and argues that reinforcement learning consistently performed well to the extent they tested. However, they explored very little about the latter approaches and humbly describes room for future exploration. All the experiments performed on Anchorhead were tuned to the step-down version of the game, a real world exposure to the game will be another great future direction of research. Also, the paper didn’t exploit much on the player modeling aspects. Finally, we feel that in the very beginning of the paper, there should have been a little introduction to the Interactive Narrative in general so that the absolute beginners can get a rough overview of the genre. Overall, we must say that it’s a great piece of work considering the meticulous details of the subject matter presented and it’s depth of explanations, and is definitely a must read for anyone diving into this field.

NB: The article is a critique on Roberts, D. L., & Isbell Jr, C. L. Declarative Optimization-Based Drama Management in Interactive Fiction.

Insights into “The behavior of tutoring systems” 

Analyzing the perspective of a practitioner vs. a research scientist

Among all the rapidly expanding facets of Computer Science, Artificial Intelligence or AI is probably the hottest area of research and development in the era we are living in. In this fast paced area of machine intelligence, sometimes even experts working in the very field get lost while trying to model something unique, something new. Building Intelligent Systems for Education has been in practice for quite a long time now(since the late 1960s), though it has not reached it’s extreme yet. More and more genius minds from AI fraternity are shifting towards this emerging field. However, unlike many of the other fields of Computer Science, this field is totally multidisciplinary in nature and often very hard to dive into a practical implementation stage for the same, even for the scientists working long in related research areas. In such a scenario, we find Kurt VanLehn’s(KVL) framework in “The behavior of tutoring systems” undoubtedly the most appropriate starting point for the people of almost all kinds of expert groups. His brilliant layout of the whole description makes it a piece of cake to understand the bigger picture of the whole area of “Intelligent Tutoring Systems(ITS)”. He primarily describes his work as a guide for both novice and experts, but we find the work equally well suited for bridging the gap between the theoretical research standpoint and practical implementation of such intelligent systems. It starts with a soft introduction which even a newbie would be able to follow without any trouble and gradually guides us through the design and implementation overviews of such systems. His descriptions are very well complimented with appropriate examples from few commercially and academically available ITS being used in practice from quite a long time. In this paper, we try to shed some lights on how the different portions of the KVL work help us understanding the core concepts of ITS while giving us enough information to start thinking about a practical implementation of such a system. However, we might not hope to be able to build a fully fledged ITS after reading the paper, which was never his intention of the work anyway, we get a very enthusiastic and structured direction of thoughts about bringing such systems into existence. We believe that it’s the most appropriate starting point for any mind diving into the sea of Intelligent Learning Environments. We summarize the comments about the different sections of the paper while showing the differences between the theoretical research scientists’ viewpoint and that of the practitioners’ in the field whenever applicable.

Core terminologies:

The paper starts with the explanations of some of the basic terminologies of ITS frameworks in a very intuitive way. The explanations are also well backed by relevant examples from some commercial ITS frameworks like Algebra Cognitive Tutor, Andes, AutoTutor, Sherlock, SQL-Tutor and Steve. These details serve as a base for understanding the rest of the paper and is very useful for anyone reading it. The theoretical research guy will find it useful to understand the rest of the theories while the practitioner guy will get an idea of what are the possible components he might probably have to design and develop. We give a very brief snapshot of the terms described here.

First it talks about the “Task Domain” and “Task” associated with the system. The former refers to the area of expertise the ITS wants it’s users to learn while the latter refers to the actual “chronological sequence” of actions that the learner has to perform on the ITS to achieve the modularized goals. Next two terms described are “Knowledge Component” and “Learning Events” which are very closely related in their meanings. The paper describes the subtle differences between the two very cleverly with appropriate examples along with the descriptions.

The last set of terms are the ones that probably serves as the spinal cord for the rest of the description. The paper describes the whole architecture as a combination of an “outer loop” and an “inner loop”. Very broadly, the outer loop iterates through the different “tasks” which the ITS wants to impose on its users. The inner loop is the iterator over the actual physical “steps” the user will perform per task assigned by the other loop. However, the inner loop is also responsible for few other things like intelligent feedback, student modeling and giving signals to outer loop for better selection of next set of tasks to be assigned. This kind of a description is so intuitive and easy to understand that even a layman can understand what’s going on inside an ITS. We feel that breaking the whole system in terms of these two loops clears up almost a major part of the whole system as to what we will be dealing with. The theoretical scientists will find it easier to map the upcoming descriptions to one of these loops while the practitioners would get a high level implementation overview. However, these notions of “inner loop” and “outer loop” has little practical usage and actual implementations will be much more complex than these simple loop structure, which the paper very humbly mentions as “In real tutoring systems, the inner loop may be more complex that the one shown in Table 2”.

Bigger Picture: The Outer Loop

The design of the outer loop decides how the system proceeds along with the learner. The main responsibility of this component is to cleverly select new problems to be presented to the learner at each step, which has to be very much learner specific in nature. The paper describes four major approaches which can serve as a baseline for designing any protocol to solve the problem by the research community. At the same time, the practitioner will find it very useful to start a basic implementation of the task selection module by using one of these basic strategies for the same, which he can later modify to incorporate many complex ways to handle it. The first two approach of task selection is very simple, the first one lets students select problems from a pool while the second one relies on the teacher model to assign the next problem in a “predetermined sequence”. The latter two approaches namely “Mastery Learning” and “Macroadaptive Learning” are somewhat more complex in nature and the description in the paper handles it very smoothly with appropriate examples. Finally, he concludes with some techniques to model the corpus of the problems which included “Human Authors” and “Problem Generators”.

Internal Dynamics: The Inner Loop

The rest of the description talks elaborately about the internal dynamics of a task which he refers to as the “inner loop”. It’s a hypothetical concept which embraces most the complexities of an ITS in terms of a simple loop that keeps running unless the learner successfully completes a particular task. The whole idea of the design of different aspects of this module is to keep students engaged in the most effective ways and reducing the possibilities of potential misbehaviors by the learners while executing the steps. It’s a natural tendency of one group of learners to abuse the facilities given to them and hence the design aspects of the inner loop are very crucial.

Among the most common services of the inner loop, giving minimal feedback is one of the most common ones. The feedback may be anything from correct, incorrect and non-optimal or unrecognized solutions. Hints are the next set of important component of an ITS. We should very clearly define the aspects of when, what and how to give the hints to the learner. The differences between all the 6 tutoring systems are explained with respect to these parameters and we get a fairly clear view of how to go about the whole hint generation mechanism after reading this section. The notion of “Help Abuse” and “Help Refusal” are well described. One of the major issues we encounter while devising a hint generation mechanism is the exact mapping of the thought process of the student model and what the hint module thinks about it. Sometimes the student might do the same problem in a correct but a different manner which ITS might reflect back as a wrong solution or sometimes student makes incorrect steps but receives minimal negative feedback. This is a very crucial issue in this regard and the author has given some nice explanations about the same and also ways to handle this. One of the ways to handle such hint mismanagement was implemented in Andes, where it asks questions back to students whenever the ITS is unsure of some step made by them. The students then answer back the ITS with some menu based fixed answers which helped Andes to narrow down on a decision about the step. The last aspect of the hint generator is the ways to generate hints. They are normally given in sequences, starting with very basic hints, it goes on giving till bottom-out hints. However, there are many other internal complications which he explains beautifully in the paper and gives pretty much everything we need to start exploring more on the hint mechanisms.

Error-specific feedbacks are yet another important area to concentrate on. Though the author has not described the insights of the “black box” error recognition system(step analyzer), it does talk about how to provide feedbacks to the learner based on the “error description” provided by the black box. The basic idea behind almost all kind of such module is to drive the students towards a “self-debugging” mode. He also describes as to when is it appropriate to present the feedback and analyzes different combinations of implementation which serves as a baseline for the practitioner to choose a design from.

One important part of all the ITS frameworks, thought not related to the above aspects, is the student modeling. This assessment of the student helps the system decide the next course of actions to be taken in order to achieve a fruitful learning experience. The author talks briefly about the coarse-grained assessments and elaborates on fine-grained assessments. He beautifully models the assessments matrices in terms of probabilities of both successful applications of learning components and in terms of failures. The details are described with example and comes very handy while designing our own tutoring system’s student model. The work also points out the common issues and questions related to the same. However, he doesn't give any pointers on how to handle those issues and we may find it little incomplete in it’s part. Finally he concludes with some discussions on delayed feedback mechanisms and on reviewing the whole solution at once paradigm of hint mechanism.

Concluding Remarks:

In his final remarks, he humbly mentions about all the topics that he missed in the literature like Evaluation of tutoring systems, Role of instructors in the process, about Step Generators and Step Analyzers, User interfaces etc. Among everything else, we feel that Evaluation of ITS frameworks is one thing which the author should have considered including in the work as we feel that it is one of the  most inevitable topics of any Artificially Intelligent System. Similarly, as we already mentioned earlier, a little more details into the black boxes of Step Analyzer and Step Generator would have also been nice. For the research fraternity it may not be of a much big of a deal as they will be reading dozens of other literatures which will cover those topics definitely. But from the point of view of the practitioner, it may become a bottleneck at times if he solely relies on this work as a starting point. However, it will be very unfair to judge the marvelous work in KVL framework just because it lacks one or two topics when it has so much more to offer already on its part. Finally, as a concluding remark, we want to say that keeping the small glitches aside, the KVL framework is undoubtedly one of the best literature in the field and we certainly recommend it to any individual irrespective of his field of interest or expertise, to have a read of the same if he or she is interested in ITS frameworks.

NB: The article is a critique on Vanlehn, K. (2006). The behavior of tutoring systems. International journal of artificial intelligence in education, 16(3), 227-265.

Potential Damage: Every idea and advice that is presented in this blog as a part of some articles are purely from my own perspective of how I deal with different challenges of my life. It's my mind map, I just write them down to relax my incessant chain of thoughts. Some you  might find too mainstream and common stuff which big shots keep telling everyday. Feel free to not read even one word after this line, not agree with them or leave your comment if you find something brutally wrong. Also don't sue me if you end up in shit listening to something I say here, because I am not a God. Happy reading and cheers!