Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Graphical Models for Game Theory
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Multi-agent algorithms for solving graphical games
Eighteenth national conference on Artificial intelligence
Multi-agent influence diagrams for representing and solving games
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Using reasoning patterns to help humans solve complex games
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Reasoning patterns in Bayesian games
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Human factors in computer decision-making
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
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What reasoning patterns do agents use to choose their actions in games? This paper studies this question in the context of Multi-Agent Influence Diagrams (MAIDs). It defines several kinds of reasoning patterns, and associates each with a pattern of paths in a MAID. We asks the question, what reasoning patterns have to hold in order for an agent to care about its decision? The answer depends on what strategies are considered for other agents' decisions. We introduce a new solution concept, called well-distinguishing (WD) strategies, that captures strategies in which all the distinctions an agent makes really make a difference. We show that when agents are playing WD strategies, all situations in which an agent cares about its decision can be captured by four reasoning patterns. We furthermore show that when one of these four patterns holds, there are some MAID parameter values such that the agent actually does care about its decision.