The complexity of Markov decision processes
Mathematics of Operations Research
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Learning an Agent's Utility Function by Observing Behavior
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Algorithms for Inverse Reinforcement Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
The Complexity of Decentralized Control of Markov Decision Processes
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Optimizing information exchange in cooperative multi-agent systems
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
The communicative multiagent team decision problem: analyzing teamwork theories and models
Journal of Artificial Intelligence Research
When do numbers really matter?
Journal of Artificial Intelligence Research
Thespian: using multi-agent fitting to craft interactive drama
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Direct manipulation like tools for designing intelligent virtual agents
Lecture Notes in Computer Science
Proactive Authoring for Interactive Drama: An Author's Assistant
IVA '07 Proceedings of the 7th international conference on Intelligent Virtual Agents
Modeling Appraisal in Theory of Mind Reasoning
IVA '08 Proceedings of the 8th international conference on Intelligent Virtual Agents
THESPIAN: An Architecture for Interactive Pedagogical Drama
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
Functional value iteration for decision-theoretic planning with general utility functions
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
PsychSim: modeling theory of mind with decision-theoretic agents
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Directorial Control in a Decision-Theoretic Framework for Interactive Narrative
ICIDS '09 Proceedings of the 2nd Joint International Conference on Interactive Digital Storytelling: Interactive Storytelling
Thespian: modeling socially normative behavior in a decision-theoretic framework
IVA'06 Proceedings of the 6th international conference on Intelligent Virtual Agents
Applying direct manipulation interfaces to customizing player character behaviour
ICEC'06 Proceedings of the 5th international conference on Entertainment Computing
Optimal Software Free Trial Strategy: The Impact of Network Externalities and Consumer Uncertainty
Information Systems Research
Toward automatic verification of multiagent systems for training simulations
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
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Decision-theoretic models have become increasingly popular as a basis for solving agent and multiagent problems, due to their ability to quantify the complex uncertainty and preferences that pervade most nontrivial domains. However, this quantitative nature also complicates the problem of constructing models that accurately represent an existing agent or multiagent system, leading to the common question, "Where do the numbers come from?" In this work, we present a method for exploiting knowledge about the qualitative structure of a problem domain to automatically derive the correct quantitative values that would generate an observed pattern of agent behavior. In particular, we propose the use of piecewise linear functions to represent probability distributions and utility functions with a structure that we can then exploit to more efficiently compute value functions. More importantly, we have designed algorithms that can (for example) take a sequence of actions and automatically generate a reward function that would generate that behavior within our agent model. This algorithm allows us to efficiently fit an agent or multiagent model to observed behavior. We illustrate the application of this framework with examples in multiagent modeling and social simulation, using decision-theoretic models drawn from the alphabet soup of existing research (e.g., MDPs, POMDPs, Dec-POMDPs, Com-MTDPs).