Multi-tier Automated Planning for Adaptive Behavior (Extended Version)

TL;DR

This paper introduces a multi-tier planning framework that supports adaptive behavior by allowing multiple assumptions and objectives, addressing the limitations of single-model planning in dynamic environments.

Contribution

It proposes a novel multi-tier planning approach that enables the synthesis of adaptive plans under varying assumptions, extending traditional planning models.

Findings

Framework supports multiple assumptions and objectives

Compilation to non-deterministic planning enables solution derivation

Highlights importance of dual fairness in planning systems

Abstract

A planning domain, as any model, is never complete and inevitably makes assumptions on the environment's dynamic. By allowing the specification of just one domain model, the knowledge engineer is only able to make one set of assumptions, and to specify a single objective-goal. Borrowing from work in Software Engineering, we propose a multi-tier framework for planning that allows the specification of different sets of assumptions, and of different corresponding objectives. The framework aims to support the synthesis of adaptive behavior so as to mitigate the intrinsic risk in any planning modeling task. After defining the multi-tier planning task and its solution concept, we show how to solve problem instances by a succinct compilation to a form of non-deterministic planning. In doing so, our technique justifies the applicability of planning with both fair and unfair actions, and the need for more efforts in developing planning systems supporting dual fairness assumptions.