Ram, Ashwin


Learning from Demonstration to Be a Good Team Member in a Role Playing Game

AAAI Conferences

We present an approach that uses learning from demonstration in a computer role playing game to create a controller for a companion team member. We describe a behavior engine that uses case-based reasoning. The behavior engine accepts observation traces of human playing decisions and produces a sequence of actions which can then be carried out by an artificial agent within the gaming environment. Our work focuses on team-based role playing games, where the agents produced by the behavior engine act as team members within a mixed human-agent team. We present the results of a study we conducted, where we assess both the quantitative and qualitative performance difference between human-only teams compared with hybrid human-agent teams. The results of our study show that human-agent teams were more successful at task completion and, for some qualitative dimensions, hybrid teams were perceived more favorably than human-only teams.


Learning Opponent Strategies through First Order Induction

AAAI Conferences

In a competitive game it is important to identify the opponent's strategy as quickly and accurately as possible so that an effective response can be planned. In this vein, this paper summarizes our work in exploring using first order inductive learning to learn rules for representing opponent strategies. Specifically, we use these learned rules to perform plan recognition and classify an opponent strategy as one of multiple learned strategies. Our experiments validate this novel approach in a simple real-time strategy game.


Reports of the AAAI 2010 Conference Workshops

AI Magazine

The AAAI-10 Workshop program was held Sunday and Monday, July 11–12, 2010 at the Westin Peachtree Plaza in Atlanta, Georgia. The AAAI-10 workshop program included 13 workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were AI and Fun, Bridging the Gap between Task and Motion Planning, Collaboratively-Built Knowledge Sources and Artificial Intelligence, Goal-Directed Autonomy, Intelligent Security, Interactive Decision Theory and Game Theory, Metacognition for Robust Social Systems, Model Checking and Artificial Intelligence, Neural-Symbolic Learning and Reasoning, Plan, Activity, and Intent Recognition, Statistical Relational AI, Visual Representations and Reasoning, and Abstraction, Reformulation, and Approximation. This article presents short summaries of those events.


Reports of the AAAI 2010 Conference Workshops

AI Magazine

The AAAI-10 Workshop program was held Sunday and Monday, July 11–12, 2010 at the Westin Peachtree Plaza in Atlanta, Georgia. The AAAI-10 workshop program included 13 workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were AI and Fun, Bridging the Gap between Task and Motion Planning, Collaboratively-Built Knowledge Sources and Artificial Intelligence, Goal-Directed Autonomy, Intelligent Security, Interactive Decision Theory and Game Theory, Metacognition for Robust Social Systems, Model Checking and Artificial Intelligence, Neural-Symbolic Learning and Reasoning, Plan, Activity, and Intent Recognition, Statistical Relational AI, Visual Representations and Reasoning, and Abstraction, Reformulation, and Approximation. This article presents short summaries of those events.


Goal-Driven Learning in the GILA Integrated Intelligence Architecture

AAAI Conferences

Goal Driven Learning (GDL) focuses on systems that determine by themselves what has to be learnt and how to learn it. Typically GDL systems use meta-reasoning capabilities over a base {\em reasoner}, identifying learning goals and devising strategies. In this paper we present a novel GDL technique to deal with complex AI systems where the meta-reasoning module has to analyze the reasoning trace of multiple components with potentially different learning paradigms. Our approach works by distributing the generation of learning strategies among the different modules instead of centralizing it in the meta-reasoner. We implemented our technique in the GILA system, that works in the airspace task orders domain, showing an increase in performance.


AAAI 1994 Spring Symposium Series Reports

AI Magazine

The Association for the Advancement of Artificial Intelligence (AAAI) held its 1994 Spring Symposium Series on 19-23 March at Stanford University, Stanford, California. This article contains summaries of 10 of the 11 symposia that were conducted: Applications of Computer Vision in Medical Image Processing; AI in Medicine: Interpreting Clinical Data; Believable Agents; Computational Organization Design; Decision-Theoretic Planning; Detecting and Resolving Errors in Manufacturing Systems; Goal-Driven Learning; Intelligent Multimedia, Multimodal Systems; Software Agents; and Toward Physical Interaction and Manipulation. Papers of most of the symposia are available as technical reports from AAAI.


AAAI 1994 Spring Symposium Series Reports

AI Magazine

The Association for the Advancement of Artificial Intelligence (AAAI) held its 1994 Spring Symposium Series on 19-23 March at Stanford University, Stanford, California. This article contains summaries of 10 of the 11 symposia that were conducted: Applications of Computer Vision in Medical Image Processing; AI in Medicine: Interpreting Clinical Data; Believable Agents; Computational Organization Design; Decision-Theoretic Planning; Detecting and Resolving Errors in Manufacturing Systems; Goal-Driven Learning; Intelligent Multimedia, Multimodal Systems; Software Agents; and Toward Physical Interaction and Manipulation. Papers of most of the symposia are available as technical reports from AAAI.


Goal-Driven Learning: Fundamental Issues: A Symposium Report

AI Magazine

In AI, psychology, and education, a growing body of research supports the view that learning is a goal-directed process. Psychological experiments show that people with varying goals process information differently, studies in education show that goals have a strong effect on what students learn, and functional arguments in machine learning support the necessity of goal-based focusing of learner effort. At the Fourteenth Annual Conference of the Cognitive Science Society, a symposium brought together researchers in AI, psychology, and education to discuss goal-driven learning. This article presents the fundamental points illuminated at the symposium, placing them in the context of open questions and current research directions in goal-driven learning.


Goal-Driven Learning: Fundamental Issues: A Symposium Report

AI Magazine

In AI, psychology, and education, a growing body of research supports the view that learning is a goal-directed process. Psychological experiments show that people with varying goals process information differently, studies in education show that goals have a strong effect on what students learn, and functional arguments in machine learning support the necessity of goal-based focusing of learner effort. At the Fourteenth Annual Conference of the Cognitive Science Society, a symposium brought together researchers in AI, psychology, and education to discuss goal-driven learning. This article presents the fundamental points illuminated at the symposium, placing them in the context of open questions and current research directions in goal-driven learning.


Basic Artificial Intelligence Research at the Georgia Institute of Technology

AI Magazine

AI research is conducted at a number of academic and research units at the Georgia Institute of Technology. Some of this research is basic in nature, and some has an applied character to it. This article briefly describes basic AI research in the College of Computing at Georgia Tech.