{"id":55,"date":"2025-03-02T15:45:11","date_gmt":"2025-03-02T14:45:11","guid":{"rendered":"https:\/\/plato2025.istc.cnr.it\/?page_id=55"},"modified":"2025-03-11T11:29:23","modified_gmt":"2025-03-11T10:29:23","slug":"description-of-the-workshop","status":"publish","type":"page","link":"https:\/\/plato2025.istc.cnr.it\/?page_id=55","title":{"rendered":"Description of the Workshop"},"content":{"rendered":"\n
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About the Workshop<\/h3>\n\n\n\n

Automated Planning<\/strong> and Ontology<\/strong> are two well-established fields of Artificial Intelligence<\/strong> (AI). The former investigates techniques to formally model and reason about the effects of actions, and decide the combinations of actions that allow an agent to achieve goals. The latter investigates techniques to formally define knowledge (by formally describing domain entities and their interrelations), allowing agents to process information about objects, and events, and incrementally build and verify beliefs. Both Automated Planning and Ontology generally rely on logic to model knowledge and organize reasoning mechanisms. They support the development of cognitive capabilities that autonomous agents need to effectively act in the real world. <\/p>\n\n\n\n

In this context, the PLanning And onTology wOrkshop<\/strong> (PLATO<\/strong>) aims at bringing together researchers in these two fields of AI to address new research challenges, share their experiences, and learn from each other. The workshop aims at investigating the synergetic contributions of technologies and methods from these two fields. There are examples in the literature that have investigated the use of Ontology to generate planning models [1,2], search effective plans [3,4], represent and reason about agents’ belief [5], contextualize plan actions and goal reasoning [6,7], and generalize unseen scenarios [8], contextualize plans and action execution to domain features, and generalize over unseen scenarios.<\/p>\n\n\n\n

The workshop is open to both application and theoretical contributions that see the integration of ontology and planning as a mechanism to enhance the efficacy of AI-based solutions.<\/p>\n<\/div>\n<\/div>\n\n\n\n

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PLATO@JOWO2025<\/h5>\n\n\n\n

The Joint Ontology WOrkshops<\/a><\/strong> (JOWO) is a venue of workshops that, together, address a wide spectrum of topics related to ontology research, ranging from Cognitive Science to Knowledge Representation, Natural Language Processing, Artificial Intelligence, Logic, Philosophy, and Linguistics.<\/p>\n\n\n\n

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JOWO is a conference of the International Association for Ontology and its Applications<\/a> (IAOA), which is a non-profit organization aiming to promote interdisciplinary research and international collaboration in formal ontology. <\/p>\n\n\n\n

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JOWO 2025 is held in conjunction with the 15th International Conference on Formal Ontology in Information Systems (FOIS)<\/a>.<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n

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Topics of Interests<\/h2>\n\n\n\n
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  • Out-of-the-box research challenges at the intersection between Automated Planning and Ontology<\/li>\n\n\n\n
  • Ontological analysis of concepts related to planning\/scheduling (e.g., capability, capacity, action, etc)<\/li>\n\n\n\n
  • Ontologies supporting tasks related to planning\/scheduling<\/li>\n\n\n\n
  • Reuse of foundational ontologies like DOLCE, BFO, UFO <\/li>\n\n\n\n
  • Semantic Web ontologies and technologies for reasoning, (FAIR) data management, interoperability (FIPA), etc<\/li>\n\n\n\n
  • Ontologies supporting the interoperability of heterogeneous planning frameworks<\/li>\n<\/ul>\n<\/div>\n<\/div>\n\n\n\n
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    • Plan Recognition, plan management, and goal reasoning<\/li>\n\n\n\n
    • Partially observable and unobservable domains<\/li>\n\n\n\n
    • Knowledge acquisition and engineering for planning and scheduling<\/li>\n\n\n\n
    • Explainability and situation assessment for contextual planning decisions<\/li>\n\n\n\n
    • Trustworthy, safety, and ethics in planning<\/li>\n\n\n\n
    • Benchmarking and evaluation metrics of plans and plan-based controllers<\/li>\n\n\n\n
    • Graph-based machine learning techniques to generalize over task and  domain variations<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n
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      (Some) References<\/h2>\n\n\n\n
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      1. Borgo, S., Cesta, A., Orlandini, A. et al. Knowledge-based adaptive agents for manufacturing domains. Engineering with Computers 35, 755\u2013779 (2019). https:\/\/doi.org\/10.1007\/s00366-018-0630-6<\/li>\n\n\n\n
      2. L. Kunze, T. Roehm and M. Beetz, “Towards semantic robot description languages,” 2011 IEEE International Conference on Robotics and Automation<\/em>, Shanghai, China, 2011, pp. 5589-5595, doi: 10.1109\/ICRA.2011.5980170.<\/li>\n\n\n\n
      3. Hartanto, R., & Hertzberg, J. (2008). Fusing DL Reasoning with HTN Planning. KI 2008: Advances in Artificial Intelligence<\/em>.<\/li>\n\n\n\n
      4. Tenorth, M., & Beetz, M. (2013). KnowRob: A knowledge processing infrastructure for cognitive-enabled robots. The International Journal of Robotics Research<\/em>, 32<\/em>(5).<\/li>\n\n\n\n
      5. Lemaignan, S., Warnier, M., Sisbot, A., Clodic, A., & Alami, R. (2017). Artificial cognition for social human-robot interaction: An implementation. Artificial Intelligence<\/em>, 247<\/em>.<\/li>\n\n\n\n
      6. Awaad, I., Kraetzschmar, G., & Hertzberg, J. (2015). The Role of Functional Affordances in Socializing Robots. International Journal of Social Robotics<\/em>, 7<\/em>.<\/li>\n\n\n\n
      7. Umbrico, A., Cesta, A., Cortellessa, G., & Orlandini, A. (2020). A Holistic Approach to Behavior Adaptation for Socially Assistive Robots. International Journal of Social Robotics<\/em>, 12<\/em>.<\/li>\n\n\n\n
      8. Andr\u00e9 Antakli et al. \u201cAJAN: An Engineering Framework for Semantic Web-Enabled Agents and Multi-Agent Systems\u201d. In: Advances in Practical Applications of Agents, Multi-Agent Systems, and Cognitive Mimetics. The PAAMS Collection.<\/li>\n<\/ol>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

        About the Workshop Automated Planning and Ontology are two well-established fields of Artificial Intelligence (AI). The former investigates techniques to formally model and reason about the effects of actions, and decide the combinations of actions that allow an agent to achieve goals. The latter investigates techniques to formally define knowledge (by formally describing domain entities […]<\/p>\n","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-55","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/plato2025.istc.cnr.it\/index.php?rest_route=\/wp\/v2\/pages\/55","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/plato2025.istc.cnr.it\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/plato2025.istc.cnr.it\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/plato2025.istc.cnr.it\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/plato2025.istc.cnr.it\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=55"}],"version-history":[{"count":42,"href":"https:\/\/plato2025.istc.cnr.it\/index.php?rest_route=\/wp\/v2\/pages\/55\/revisions"}],"predecessor-version":[{"id":198,"href":"https:\/\/plato2025.istc.cnr.it\/index.php?rest_route=\/wp\/v2\/pages\/55\/revisions\/198"}],"wp:attachment":[{"href":"https:\/\/plato2025.istc.cnr.it\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=55"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}