1 May

Argumentation-Based Information Exchange in Prediction Markets

Posted by cognitivecomputing in Agents, Learning. Tagged: , , .

We investigate how argumentation processes among a group of agents may affect the outcome of group judgments. In particular we focus on prediction markets (also called information markets). We investigate how the existence of social networks (that allow agents to argue with one another to improve their individual predictions) effect on group judgments.

Social networks allow agents to exchange information about the group judgment by arguing about the most likely choice based on their individual experience. We develop an argumentation-based deliberation process by which the agents acquire new and relevant information. Finally, we experimentally assess how different social network connectivity affect group judgment.

Read the paper:

Argumentation-based Information Exchange in Prediction Markets

by Santi Ontañón and Enric Plaza

in ArgMAS 2008, pp. 181 – 196
www.cc.gatech.edu/faculty/ashwin/papers/er-08-12.pdf

3 Apr

Case-Based Reasoning for Game AI

Posted by cognitivecomputing in Game AI, Learning, Talks. Tagged: , , , .

Computer games are an increasingly popular application for Artificial Intelligence (AI) research, and conversely AI is an increasingly popular selling point for commercial games. Although games are typically associated with entertainment applications, there are many “serious” applications of gaming, including military, corporate, and advertising applications. There are also what the so called “humane” gaming applications—interactive tools for medical training, educational games, and games that reflect social consciousness or advocate for a cause. Game AI is the effort of taking computer games beyond scripted interactions, however complex, into the arena of truly interactive systems that are responsive, adaptive, and intelligent. Such systems learn about the player(s) during game play, adapt their own behaviors beyond the pre-programmed set provided by the game author, and interactively develop and provide a richer experience to the player(s).

In this talk, I discuss a range of CBR approaches for Game AI. I discuss differences and similarities between character-level AI (in embedded NPCs, for example) and game-level AI (in the drama manager or game director, for example). I explain why the AI must reason at multiple levels, including reactive, tactical, strategic, rhetorical, and meta, and propose a CBR architecture that lets us design and coordinate real-time AIs operating asynchronously at all these levels. I conclude with a brief discussion on the very idea of Game AI: is it feasible? realistic? and would we call it “intelligence” if we could implement all this stuff?

View the talk:

Google Tech Talk: Case-Based Reasoning for Game AI

by Ashwin Ram

Google Tech Talk, Mountain View, CA, April 2008
www.youtube.com/watch?v=s9G7DRTuB5s

1 Mar

Discovering Semantic Biomedical Relations Utilizing The Web

Posted by cognitivecomputing in Health & Wellness, Language, Web / Web 2.0. Tagged: , , , .

To realize the vision of a Semantic Web for Life Sciences, discovering relations between resources is essential. It is very difficult to automatically extract relations from Web pages expressed in natural language formats. On the other hand, because of the explosive growth of information, it is difficult to manually extract the relations. In this paper we present techniques to automatically discover relations between biomedical resources from the Web. For this purpose we retrieve relevant information from Web Search engines and Pubmed database using various lexico-syntactic patterns as queries over SOAP web services. The patterns are initially handcrafted but can be progressively learnt. The extracted relations can be used to construct and augment ontologies and knowledge bases. Experiments are presented for general biomedical relation discovery and domain specific search to show the usefulness of our technique.

Read the paper:

Discovering Semantic Biomedical Relations utilizing the Web

by Saurav Sahay, Sougata Mukherjea, Eugene Agichtein, Ernie Garcia, Sham Navathe, Ashwin Ram

ACM Transactions on Knowledge Discovery from Data, 2(1):3, 2008
www.cc.gatech.edu/faculty/ashwin/papers/er-08-01.pdf

20 Feb

Adaptive Computer Games: Easing the Authorial Burden

Posted by cognitivecomputing in Agents, Game AI, Learning. Tagged: , , , , , .

Game designers usually create AI behaviors by writing scripts that describe the reactions to all imaginable circumstances within the confines of the game world. The AI Programming Wisdom series provides a good overview of current scripting techniques used in the game industry. Scripting is expensive and it’s hard to plan. So, behaviors could be repetitive (resulting in breaking the atmosphere) or behaviors could fail to achieve their desired purpose. On one hand, creating AI with a rich behavior set requires a great deal of engineering effort on the part of game developers. On the other hand, the rich and dynamic nature of game worlds makes it hard to imagine and plan for all possible scenarios. When behaviors fail to achieve their desired purpose, the game AI is unable to identify such failure and will continue executing them. The techniques described in this article specifically deal with these issues.

Behavior (or script) creation for computer games typically involves two steps: a) generating a first version of behaviors using a programming language, b) debugging and adapting the behavior via experimentation. In this article we present techniques that aim at assisting the author from carrying out these two steps manually: behavior learning and behavior adaptation.

In the behavior learning process, the game developers can specify the AI behavior by demonstrating it to the system instead of having to code the behavior using a programming language. The system extracts behaviors from these expert demonstrations and stores them. Then, at performance time, the system retrieves appropriate behaviors observed from the expert and revises them in response to the current situation it is dealing with (i.e., to the current game state).

In the behavior adaptation process, the system monitors the performance of these learned behaviors at runtime. The system keeps track of the status of the executing behaviors, infer from their execution trace what might be wrong, and perform appropriate adaptations to the behaviors once the game is over. This approach to behavior transformation enables the game AI to reflect on the issues in the learnt behaviors from expert demonstration and revises them after post analysis of things that went wrong during the game. These set of techniques allow non-AI experts to define behaviors through demonstration that can then be adapted to different situations thereby reducing the development effort required to address all contingencies in a complex game.

Read the paper:

Adaptive Computer Games: Easing the Authorial Burden

by Manish Mehta, Santi Ontañón, Ashwin Ram

AI Game Programming Wisdom 4 (AIGPW4), Steve Rabin (editor), Charles River Media, 2008
www.cc.gatech.edu/faculty/ashwin/papers/er-08-03.pdf

10 Nov

Driving Interactive Drama Research through Building Complete Systems

Posted by cognitivecomputing in Agents, Game AI. Tagged: , , .

Interactive drama presents one of the most challenging applications of autonomous characters, requiring characters to simultaneously engage in moment-by-moment personality-rich physical behavior, exhibit conversational competencies, and participate in a dynamically developing story arc. One way to advance the field and continue to make exciting progress is to develop building blocks needed for creating these interactive experiences that are situated in a complete system. Our research goals presented in this paper are driven by this perspective of developing a complete interactive drama architecture. Specifically, we discuss the different research challenges that we are interested in pursuing at the different building blocks required to build a complete interactive drama. We also discuss the interactive drama domain we are developing and present our initial steps in handling the research challenges.

Read the paper:

Driving Interactive Drama Research through Building Complete Systems

by Manish Mehta, Santi Ontañón, Ashwin Ram

AAAI-07 Spring Symposium on Intelligent Narrative Technologies, Arlington, VA, November 2009
www.cc.gatech.edu/faculty/ashwin/papers/er-07-14.pdf

9 Nov

Drama Management Evaluation for Interactive Fiction Games

Posted by cognitivecomputing in Game AI. Tagged: , .

A growing research community is working towards employing drama management components in interactive story-based games. These components gently guide the story towards a narrative arc that improves the player’s experience. However, the success of drama management approaches has not been evaluated using human players in a real game implementation. In this paper, we evaluate our drama management approach deployed in our own implementation of an interactive fiction game Anchorhead. Our approach uses player feedback as a basis for guiding the personalization of the interaction. The results indicate that our Drama Manager (DM) aids in providing a better overall experience for the players while guiding them through their interaction. Based on this work, we suggest that the strategies employed by the DM should take into account the player’s previous playing experience with the current game as well as his general game-playing experience.

Read the paper:

Drama Management Evaluation for Interactive Fiction Games

by Manu Sharma, Santi Ontañón, Manish Mehta, Ashwin Ram

AAAI-07 Spring Symposium on Intelligent Narrative Technologies, Arlington, VA, November 2007
www.cc.gatech.edu/faculty/ashwin/papers/er-07-15.pdf

10 Oct

Semantic Annotation and Inference for Medical Knowledge Discovery

Posted by cognitivecomputing in Health & Wellness, Language, Learning. Tagged: , , .

We describe our vision for a new generation medical knowledge annotation and acquisition system called SENTIENT-MD (Semantic Annotation and Inference for Medical Knowledge Discovery). Key aspects of our vision include deep Natural Language Processing techniques to abstract the text into a more semantically meaningful representation guided by domain ontology. In particular, we introduce a notion of semantic fitness to model an optimal level of abstract representation for a text fragment given a domain ontology. We apply this notion to appropriately condense and merge nodes in semantically annotated syntactic parse trees. These transformed semantically annotated trees are more amenable to analysis and inference for abstract knowledge discovery, such as for automatically inferring general medical rules for enhancing an expert system for nuclear cardiology. This work is a part of a long term research effort on continuously mining medical literature for automatic clinical decision support.

Read the paper:

Semantic Annotation and Inference for Medical Knowledge Discovery

by Saurav Sahay, Eugene Agichtein, Baoli Li, Ernie Garcia, Ashwin Ram

NSF Symposium on Next Generation of Data Mining (NGDM-07), Baltimore, MD, October 2007
www.cc.gatech.edu/faculty/ashwin/papers/er-07-16.pdf

31 Aug

Adapting Associative Classification to Text Categorization

Posted by cognitivecomputing in Language. Tagged: , .

Associative classification, which originates from numerical data mining, has been applied to deal with text data recently. Text data is firstly digitalized to database of transactions, and then training and prediction is actually conducted on the derived numerical dataset. This intuitive strategy has demonstrated quite good performance. However, it doesn’t take into consideration the inherent characteristics of text data as much as possible, although it has to deal with some specific problems of text data such as lemmatizing and stemming during digitalization. In this paper, we propose a bottom-up strategy to adapt associative classification to text categorization, in which we take into account structure information of text. Experiments on Reuters-21578 dataset show that the proposed strategy can make use of text structure information and achieve better performance.

Read the paper:

Adapting Associative Classification to Text Categorization

by Baoli Li, Neha Sugandh, Ernie Garcia, Ashwin Ram

ACM Conference on Document Engineering (ACM-DocEng-07), Winnipeg, Canada, August 2007
www.cc.gatech.edu/faculty/ashwin/papers/er-07-13.pdf

16 Aug

Case-Based Planning and Execution for Real-Time Strategy Games

Posted by cognitivecomputing in Game AI. Tagged: , , , , .

Artificial Intelligence techniques have been successfully applied to several computer games. However in some kinds of computer games, like real-time strategy (RTS) games, traditional artificial intelligence techniques fail to play at a human level because of the vast search spaces that they entail. In this paper we present a real-time case based planning and execution approach designed to deal with RTS games. We propose to extract behavioral knowledge from expert demonstrations in form of individual cases. This knowledge can be reused via a case based behavior generator that proposes behaviors to achieve the specific open goals in the current plan. Specifically, we applied our technique to the WARGUS domain with promising results.

Read the paper:

Case-Based Planning and Execution for Real-Time Strategy Games

by Santi Ontañón, Kinshuk Mishra, Neha Sugandh, Ashwin Ram

Seventh International Conference on Case-Based Reasoning (ICCBR-07), Belfast, Northern Ireland, August 2007
www.cc.gatech.edu/faculty/ashwin/papers/er-07-11.pdf

8 Jul

A Cognitive Model of Problem-Based Learning and its Application to Educational Software Design

Posted by cognitivecomputing in Education, Learning, Web / Web 2.0. Tagged: , .

Problem-based learning (PBL) is a constructivist pedagogy in which students learn in small groups by working on real-world problems. Despite its many benefits, however, this pedagogy is still not widely used in K-16 classrooms, especially with large numbers of students. Traditional human-facilitated PBL places intense demands on faculty to facilitate problem-solving sessions with small groups of students; on the other hand, most educational technologies do not provide PBL’s collaborative problem-solving experience.

We propose a cognitive model of the problem-based learning process. We present a software environment called CaseBook that allows instructors to author and share problems and provides students with a pedagogically-sound PBL experience based on the cognitive model. CaseBook has been used in high school and undergraduatefrom two studies in actual classrooms.

Read the paper:

A Cognitive Model of Problem-Based Learning and its Application to Educational Software Design

by Ashwin Ram, Preetha Ram, Jennifer Holzmann, Chris Sprague

International Conference on e-Learning (eLearn-07), Lisbon, Portugal, July 2007. Also presented at Eleventh International Conference on Human-Computer Interaction (INTERACT-07), Panel on Human-Centric e-Learning, Rio de Janeiro, Brazil, September 2007.

www.cc.gatech.edu/faculty/ashwin/papers/er-07-05.pdf

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