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

Ramon Fraga Pereira, Nir Oren, Felipe Meneguzzi

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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

Bibliographical note

Funding Information:
Preliminary versions of parts of this article appeared as a two-page extended abstract [29] and a workshop paper [31]. This article expands the problem formulation and formalisation, the descriptions and discussion of the heuristics and their implications for the technique, working examples and explanations, and experimentation. This work was financed by the Coordenação de Aperfeiçoamento de Pessoal de Nivel Superior (Brazil, Finance Code 001). F. Meneguzzi thanks CNPq for partial financial support under its PQ fellowship, grant number 305969/2016-1. Authors’ addresses: R. F. Pereira and F. Meneguzzi, School of Technology, PUCRS, Porto Alegre, Brazil; emails:,; N. Oren, Department of Computing Science, University of Aberdeen, Aberdeen, Scotland; email: Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from © 2020 Association for Computing Machinery. 2157-6904/2020/01-ART23 $15.00


  • 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


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