Constructing Belief Networks to Evaluate Plans

TL;DR

This paper presents methods for constructing belief networks to evaluate complex plans with features like context dependence, contingencies, multiple agents, and temporal constraints, enhancing plan assessment capabilities.

Contribution

It introduces techniques for building belief networks that handle diverse complicating features in plans, improving evaluation accuracy.

Findings

Effective handling of context-dependent consequences

Incorporation of temporal and contingency information

Support for multi-agent and hierarchical plans

Abstract

This paper examines the problem of constructing belief networks to evaluate plans produced by an knowledge-based planner. Techniques are presented for handling various types of complicating plan features. These include plans with context-dependent consequences, indirect consequences, actions with preconditions that must be true during the execution of an action, contingencies, multiple levels of abstraction multiple execution agents with partially-ordered and temporally overlapping actions, and plans which reference specific times and time durations.