Using Sub-Optimal Plan Detection to Identify Commitment Abandonment in Discrete Environments

Ramon Fraga Pereira, Nir Oren, Felipe Meneguzzi

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


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 fully observable plan execution trace. Such capability is very important in domains where multiple agents cooperate and delegate tasks among themselves, such as 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 a creditor can use our technique to determine—by observing a trace—whether a debtor 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.
Original languageEnglish
Article number23
Number of pages26
JournalACM Transactions on Intelligent Systems and Technology
Issue number2
Publication statusPublished - Jan 2020


  • Commitments
  • plan abandonment
  • plan execution
  • landmarks
  • domain-independent heuristics
  • optimal plan
  • sub-optimal plan
  • Domainindependent heuristics
  • Plan abandonment
  • Plan execution
  • Landmarks
  • Sub-optimal plan
  • Optimal plan


Dive into the research topics of 'Using Sub-Optimal Plan Detection to Identify Commitment Abandonment in Discrete Environments'. Together they form a unique fingerprint.

Cite this