Using Sub-Optimal Plan Detection to Identify Commitment Abandonment in Discrete Environments
This addresses the need for agents to infer commitment abandonment in cooperative multi-agent systems, such as task delegation, but it is incremental as it builds on existing planning heuristics.
The paper tackles the problem of determining whether an agent has abandoned a goal by observing plan traces, using domain-independent heuristics to identify sub-optimal action steps, and shows that the approach infers sub-optimal steps with very high accuracy and detects commitment abandonment in nearly all cases.
Assessing whether an agent has abandoned a goal or is actively pursuing it is important when multiple agents are trying to achieve joint goals, or when agents commit to achieving goals for each other. Making such a determination for a single goal by observing only plan traces is not trivial as agents often deviate from optimal plans for various reasons, including the pursuit of multiple goals or the inability to act optimally. In this article, we develop an approach based on domain independent heuristics from automated planning, landmarks, and fact partitions to identify sub-optimal action steps - with respect to a plan - within a plan execution trace. Such capability is very important in domains where multiple agents cooperate and delegate tasks among themselves, e.g. through social commitments, and need to ensure that a delegating agent can infer whether or not another agent is actually progressing towards a delegated task. We demonstrate how an agent can use our technique to determine - by observing a trace - whether an agent is honouring a commitment. We empirically show, for a number of representative domains, that our approach infers sub-optimal action steps with very high accuracy and detects commitment abandonment in nearly all cases.