2Planning for Contingencies: A Decision-based Approach

TL;DR

This paper introduces Cassandra, a partial-order contingency planner that explicitly incorporates decision steps to handle uncertainty, enabling the construction of flexible plans that adapt based on acquired information.

Contribution

It presents Cassandra, a novel contingency planning approach that explicitly models decision points and separates information gathering from decision making.

Findings

Cassandra effectively constructs contingency plans with explicit decision steps.

The approach allows flexible plan execution based on real-time information.

It provides a solid foundation for integrating various decision-making procedures.

Abstract

A fundamental assumption made by classical AI planners is that there is no uncertainty in the world: the planner has full knowledge of the conditions under which the plan will be executed and the outcome of every action is fully predictable. These planners cannot therefore construct contingency plans, i.e., plans in which different actions are performed in different circumstances. In this paper we discuss some issues that arise in the representation and construction of contingency plans and describe Cassandra, a partial-order contingency planner. Cassandra uses explicit decision-steps that enable the agent executing the plan to decide which plan branch to follow. The decision-steps in a plan result in subgoals to acquire knowledge, which are planned for in the same way as any other subgoals. Cassandra thus distinguishes the process of gathering information from the process of making decisions. The explicit representation of decisions in Cassandra allows a coherent approach to the problems of contingent planning, and provides a solid base for extensions such as the use of different decision-making procedures.