{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T18:49:37Z","timestamp":1766515777655,"version":"3.40.5"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"type":"electronic","value":"9781643685489"}],"license":[{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,10,16]]},"abstract":"<jats:p>Autonomous robots are agents that interact with the environment and perform tasks using their own abilities (i.e., skills) without continuous human intervention. However, in real-life scenarios, intelligent robots also need to discover the effects of their actions and understand how to save them for future use. This task appears time-consuming and very challenging, especially in a social environment populated by people who typically modify their behaviors based on the context and can dynamically impact the robot\u2019s decision-making process. This paper aims to investigate the feasibility of autonomously creating an abstract representation of the domain knowledge from the data acquired during the robot\u2019s exploration, inferring causal-effect relations between the executed actions, and learning context-aware symbols that describe the environment states at high level, ultimately producing a PDDL-based description of the domain. With this purpose, a new framework that relies on ROS, the standard de-facto in robotics, and ROSPlan has been developed to facilitate the transfer into several robotic platforms. Preliminary results suggest the possibility of describing the robot\u2019s experience per option via context-based symbols that are consistently learned by the system from a few data samples.<\/jats:p>","DOI":"10.3233\/faia241021","type":"book-chapter","created":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T13:56:44Z","timestamp":1729173404000},"source":"Crossref","is-referenced-by-count":2,"title":["An Empirical Study of Grounding PPDDL Plans for AI-Driven Robots in Social Environment"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8937-9739","authenticated-orcid":false,"given":"Gloria","family":"Beraldo","sequence":"first","affiliation":[{"name":"Institute of Cognitive Sciences and Technologies, National Research Council of Italy (CNR-ISTC)"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4370-7156","authenticated-orcid":false,"given":"Angelo","family":"Oddi","sequence":"additional","affiliation":[{"name":"Institute of Cognitive Sciences and Technologies, National Research Council of Italy (CNR-ISTC)"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2420-4713","authenticated-orcid":false,"given":"Riccardo","family":"Rasconi","sequence":"additional","affiliation":[{"name":"Institute of Cognitive Sciences and Technologies, National Research Council of Italy (CNR-ISTC)"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2024"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA241021","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T13:56:44Z","timestamp":1729173404000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA241021"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,16]]},"ISBN":["9781643685489"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia241021","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"type":"print","value":"0922-6389"},{"type":"electronic","value":"1879-8314"}],"subject":[],"published":{"date-parts":[[2024,10,16]]}}}