Explainability Framework for Policy-Aware Autonomous Agents
In the field of Artificial Intelligence, an agent is a system which is able to autonomously make decisions in order to reach a desired goal. As these systems grow more prevalent in our day-to-day lives, there has been an increased need to add explainability features which can provide an account for an agent's behavior. We therefore propose a framework that outlines how to produce comprehensible explanations for policy-aware agents, or agents which have rule-enforcing policies incorporated in their decision-making framework. This framework is designed using insights from the social sciences on how to produce good explanations. It is implemented in the Answer Set Programming language while ...
| Authors | Heather Merhout, Daniela Inclezan |
|---|---|
| Primary category | cs.LO |
| Published | 23 July 2026 |
| Last updated at source | 23 July 2026 |
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