Archive for the ‘Agents’ Category

1 Jul

On Similarity Measures based on a Refinement Lattice

Posted by cognitivecomputing in Agents. Tagged: , .

Retrieval of structured cases using similarity has been studied in CBR but there has been less activity on defining similarity on description logics (DL). We present an approach that allows us to present two similarity measures for feature logics, a subfamily of DLs, based on the concept of “refinement lattice”. The first one is based on computing the anti-unification (AU) of two cases to assess the amount of shared information. The second measure decomposes the cases into a set of independent “properties”, and then assesses how many of these properties are shared between the two cases. Moreover, we show that the defined measures are applicable to any representation language for which a refinement lattice can be defined. We empirically evaluate our measures comparing them to other measures in the literature in a variety of relational data sets showing very good results.

Read the paper:

On Similarity Measures based on a Refinement Lattice

by Santi Ontañón and Enric Plaza

in ICCBR 2009, LNAI 5650, pp 240 – 255
www.cc.gatech.edu/faculty/ashwin/papers/er-09-11.pdf

22 May

Emotional Memory and Adaptive Personalities

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

Believable agents designed for long-term interaction with human users need to adapt to them in a way which appears emotionally plausible while maintaining a consistent personality. For short-term interactions in restricted environments, scripting and state machine techniques can create agents with emotion and personality, but these methods are labor intensive, hard to extend, and brittle in new environments. Fortunately, research in memory, emotion and personality in humans and animals points to a solution to this problem. Emotions focus an animal’s attention on things it needs to care about, and strong emotions trigger enhanced formation of memory, enabling the animal to adapt its emotional response to the objects and situations in its environment. In humans this process becomes reflective: emotional stress or frustration can trigger re-evaluating past behavior with respect to personal standards, which in turn can lead to setting new strategies or goals.

To aid the authoring of adaptive agents, we present an artificial intelligence model inspired by these psychological results in which an emotion model triggers case-based emotional preference learning and behavioral adaptation guided by personality models. Our tests of this model on robot pets and embodied characters show that emotional adaptation can extend the range and increase the behavioral sophistication of an agent without the need for authoring additional hand-crafted behaviors.

Read the paper:

Emotional Memory and Adaptive Personalities

by Anthony Francis, Manish Mehta, Ashwin Ram

Handbook of Research on Synthetic Emotions and Sociable Robotics: New Applications in Affective Computing and Artificial Intelligence, IGI Global, 2009
www.cc.gatech.edu/faculty/ashwin/papers/er-08-10.pdf

26 Nov

Developing a Drama Management Architecture for Interactive Fiction Games

Posted by cognitivecomputing in Agents, 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. In this paper we present our Drama Management architecture for real-time interactive story games that has been connected to a real graphical interactive story based on the Anchorhead game. We also report on the natural language understanding system that has been incorporated in the system and report on a user study with an implementation of our DM architecture.

Developing a Drama Management Architecture for Interactive Fiction Games

by Santi Ontañón, Abhishek Jain, Manish Mehta, Ashwin Ram

1st Joint International Conference on Interactive Digital Storytelling (ICIDS-08), Erfurt, Germany, November 2008
www.cc.gatech.edu/faculty/ashwin/papers/er-08-11.pdf

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

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

8 Jun

Emotionally Driven Natural Language Generation for Personality Rich Characters in Interactive Games

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

Natural Language Generation for personality rich characters represents one of the important directions for believable agents research. The typical approach to interactive NLG is to hand-author textual responses to different situations. In this paper we address NLG for interactive games. Specifically, we present a novel template-based system that provides two distinct advantages over existing systems. First, our system not only works for dialogue, but enables a character’s personality and emotional state to influence the feel of the utterance. Second, our templates are resuable across characters, thus decreasing the burden on the game author. We briefly describe our system and present results of a preliminary evaluation study.

Read the paper:

Emotionally Driven Natural Language Generation for Personality Rich Characters in Interactive Games

by Christina Strong, Kinshuk Mishra, Manish Mehta, Alistair Jones, Ashwin Ram

Third Conference on Artificial Intelligence for Interactive Digital Entertainment (AIIDE-07), Stanford, CA, June 2007
www.cc.gatech.edu/faculty/ashwin/papers/er-07-09.pdf

8 May

Artificial Intelligence for Adaptive Computer Games

Posted by cognitivecomputing in Agents, 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, there are many “serious” applications of gaming, including military, corporate, and advertising applications. There are also so-called “humane” gaming applications for medical training, educational games, and games that reflect social consciousness or advocate for a cause. Game AI is the effort of going 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).

The long-term goal of our research is to develop artificial intelligence techniques that can have a significant impact in the game industry. We present a list of challenges and research opportunities in developing techniques that can be used by computer game developers. We discuss three Case Based Reasoning (CBR) approaches to achieve adaptability in games: automatic behavior adaptation for believable characters; drama management and user modeling for interactive stories; and strategic behavior planning for real-time strategy games.

Read the paper:

Artificial Intelligence for Adaptive Computer Games

by Ashwin Ram, Santi Ontañón, Manish Mehta

Invited talk at the 20th International FLAIRS Conference on Artificial Intelligence (FLAIRS-07), Special Track on Case-Based Reasoning, Key West, FL, May 2007
www.cc.gatech.edu/faculty/ashwin/papers/er-07-04.pdf

1 May

Learning and Joint Deliberation through Argumentation in Multi-Agent Systems

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

We present an argumentation framework for learning agents (AMAL) designed for two purposes: (1) for joint deliberation, and (2) for learning from communication.  The AMAL framework is completely based on learning from examples: the argument preference relation, the argument generation policy, and the counterargument generation policy are case-based techniques.

For joint deliberation, learning agents share their experience by forming a committee to decide upon some joint decision. We experimentally show that the argumentation among committees of agents improves both the individual and joint performance. For learning from communication, an agent engages into arguing with other agents in order to contrast its individual hypotheses and receive counterexamples; the argumentation process improves their learning
scope and individual performance.

Read the paper:

Learning and Joint Deliberation through Argumentation in Multi-Agent Systems

by Santi Ontañón and Enric Plaza

in Autonomous Agents and Multi-Agent Systems (AAMAS 2007), pp. 971-978
www.cc.gatech.edu/faculty/ashwin/papers/er-07-19.pdf

8 Jan

Towards Runtime Behavior Adaptation for Embodied Characters

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

Typically, autonomous believable agents are implemented using static, hand-authored reactive behaviors or scripts. This hand-authoring allows designers to craft expressive behavior for characters, but can lead to excessive authorial burden, as well as result in characters that are brittle to changing world dynamics.

In this paper we present an approach for the runtime adaptation of reactive behaviors for autonomous believable characters. Extending transformational planning, our system allows autonomous characters to monitor and reason about their behavior execution, and to use this reasoning to dynamically rewrite their behaviors. In our evaluation, we transplant two characters in a sample tag game from the original world they were written for into a different one, resulting in behavior that violates the author intended personality. The reasoning layer successfully adapts the character’s behaviors so as to bring its long-term behavior back into agreement with its personality.

Towards Runtime Behavior Adaptation for Embodied Characters

by Peng Zang, Manish Mehta, Michael Mateas, Ashwin Ram

International Joint Conference on Artificial Intelligence (IJCAI-07), Hyderabad, India, January 2007
www.cc.gatech.edu/faculty/ashwin/papers/er-07-02.pdf
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