SOAR: an architecture for general intelligence
Artificial Intelligence
Quantifying inductive bias: AI learning algorithms and Valiant's learning framework
Artificial Intelligence
Developing knowledge-based systems: reorganizing the system development life cycle
Communications of the ACM
Technical Note: \cal Q-Learning
Machine Learning
Evaluation of expert system testing methods
Communications of the ACM
HTN planning: complexity and expressivity
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Implementing the Sisyphus-93 task using Soar/TAQL
International Journal of Human-Computer Studies - Special issue: the Sisyphus-VT initiative
The GOMS family of user interface analysis techniques: comparison and contrast
ACM Transactions on Computer-Human Interaction (TOCHI)
Analysis of notions of diagnosis
Artificial Intelligence
Toward the holodeck: integrating graphics, sound, character and story
Proceedings of the fifth international conference on Autonomous agents
Machine Learning
Expert Systems
Bayesian Models for Keyhole Plan Recognition in an Adventure Game
User Modeling and User-Adapted Interaction
Verification and Validation of Knowledge-Based Systems
IEEE Transactions on Knowledge and Data Engineering
An architecture for Real-Time Reasoning and System Control
IEEE Expert: Intelligent Systems and Their Applications
A Task-Based Methodology for Specifying Expert Systems
IEEE Expert: Intelligent Systems and Their Applications
Toward a New Generation of Virtual Humans for Interactive Experiences
IEEE Intelligent Systems
Learning Hierarchical Performance Knowledge by Observation
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Toward a Methodology for AI Architecture Evaluation: Comparing Soar and CLIPS
ATAL '99 6th International Workshop on Intelligent Agents VI, Agent Theories, Architectures, and Languages (ATAL),
State abstraction for programmable reinforcement learning agents
Eighteenth national conference on Artificial intelligence
Negotiation over tasks in hybrid human-agent teams for simulation-based training
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Recent Advances in Hierarchical Reinforcement Learning
Discrete Event Dynamic Systems
Knowledge maintenance: the state of the art
The Knowledge Engineering Review
Apprenticeship learning via inverse reinforcement learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
State-Space Reduction Techniques in Agent Verification
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
Temporal Development Methods for Agent-Based
Autonomous Agents and Multi-Agent Systems
S-assess: a library for behavioral self-assessment
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Verifying Multi-agent Programs by Model Checking
Autonomous Agents and Multi-Agent Systems
Hierarchical reinforcement learning with the MAXQ value function decomposition
Journal of Artificial Intelligence Research
Accelerating reinforcement learning through implicit imitation
Journal of Artificial Intelligence Research
Monitoring teams by overhearing: a multi-agent plan-recognition approach
Journal of Artificial Intelligence Research
Bayesian inverse reinforcement learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Behavior bounding: toward effective comparisons of agents & humans
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Concurrent hierarchical reinforcement learning
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Analysis of daily-living dynamics
Journal of Ambient Intelligence and Smart Environments - Home-based Health and Wellness Measurement and Monitoring
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In this paper, we explore methods for comparing agent behavior with human behavior to assist with validation. Our exploration begins by considering a simple method of behavior comparison. Motivated by shortcomings in this initial approach, we introduce behavior bounding, an automated model-based approach for comparing behavior that is inspired, in part, by Mitchell's Version Spaces. We show that behavior bounding can be used to compactly represent both human and agent behavior. We argue that relatively low amounts of human effort are required to build, maintain, and use the data structures that underlie behavior bounding, and we provide a theoretical basis for these arguments using notions of PAC Learnability. Next, we show empirical results indicating that this approach is efiective at identifying differences in certain types of behaviors and that it performs well when compared against our initial benchmark methods. Finally, we demonstrate that behavior bounding can produce information that allows developers to identify and fix problems in an agent's behavior much more efficiently than standard debugging techniques.