APIA: An Architecture for Policy-Aware Intentional Agents

TL;DR

The paper presents APIA, an architecture for policy-aware intentional agents that integrate intention-driven behavior with policy compliance, leveraging answer set programming for reasoning.

Contribution

It extends the AIA architecture with policy compliance features using AOPL, enabling agents to reason about intentions and policies simultaneously.

Findings

APIA effectively integrates policy compliance with intention-driven behavior.

Agents can reason about policies using answer set programming.

The architecture supports multiple behavior modes based on policy adherence.

Abstract

This paper introduces the APIA architecture for policy-aware intentional agents. These agents, acting in changing environments, are driven by intentions and yet abide by domain-relevant policies. This work leverages the AIA architecture for intention-driven intelligent agents by Blount, Gelfond, and Balduccini. It expands AIA with notions of policy compliance for authorization and obligation policies specified in the language AOPL by Gelfond and Lobo. APIA introduces various agent behavior modes, corresponding to different levels of adherence to policies. APIA reasoning tasks are reduced to computing answer sets using the Clingo solver and its Python API.