Fitting and Compilation of Multiagent Models through Piecewise Linear Functions

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 ...

• Type: Conference
• Year: 2004
• Where: ATAL
• Authors: David V. Pynadath, Stacy Marsella
• Keywords: ATAL 2004, Decision-theoretic Models, Multiagent, Multiagent Problems