Monitoring plan optimality using landmarks and domain-independent heuristics

Ramon Fraga Pereira, Nir Oren, Felipe Meneguzzi

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

5 Citations (Scopus)


When acting, agents may deviate from the optimal plan, either because they are not perfect optimizers or because they interleave multiple unrelated tasks. In this paper, we detect such deviations by analyzing a set of observations and a monitored goal to determine if an observed agent's actions contribute towards achieving the goal. We address this problem without pre-defined static plan libraries, and instead use a planning domain definition to represent the problem and the expected agent behavior. At the core of our approach, we exploit domain-independent heuristics for estimating the goal distance, incorporating the concept of landmarks (actions which all plans must undertake if they are to achieve the goal). We evaluate the resulting approach empirically using several known planning domains, and demonstrate that our approach effectively detects such deviations.

Original languageEnglish
Title of host publicationAAAI Workshop - Technical Report
PublisherAI Access Foundation
Number of pages7
VolumeWS-17-01 - WS-17-15
ISBN (Electronic)9781577357865
Publication statusPublished - 2017
Event31st AAAI Conference on Artificial Intelligence, AAAI 2017 - San Francisco, United States
Duration: 4 Feb 20175 Feb 2017


Conference31st AAAI Conference on Artificial Intelligence, AAAI 2017
Country/TerritoryUnited States
CitySan Francisco


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