[DL] Spotlight Seminar on AI - ALFONSO GEREVINI - JANUARY 31

[Chiara Ghidini]

[* Apologies in case of multiple posting *]

The Italian Association for Artificial Intelligence is pleased to announce the next seminar of its Spotlight Seminars on AI initiative (https://aixia.it/incontri/autumn2022/ <https://aixia.it/incontri/autumn2022/>):

January, 31 – 5:00PM (CET)

Title: On Modeling and Learning Domain Knowledge in AI Planning

Speaker: ALFONSO EMILIO GEREVINI, University of Brescia

The aim of the seminar series is to illustrate, explore and discuss current scientific challenges, trends, and possibilities in all branches of our articulated research field. The seminars will be held virtually on the YouTube channel of the Association (https://www.youtube.com/c/AIxIAit <https://www.youtube.com/c/AIxIAit>), on a monthly basis (and made permanently available on that channel), by leading Italian researchers as well as by top international scientists. 

The seminars are mainly aimed at a broad audience interested in AI research, and they are also included in the Italian PhD programme in Artificial Intelligence; indeed, AIxIA warmly encourages the attendance of young scientists and PhD students. 

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Bio: Alfonso Emilio Gerevini is Professor of Computer Science at the University of Brescia, where he leads the research group of AI Planning and Machine Learning. Previously he was a researcher at IRST-FBK, University of Rochester, and University of Freiburg. His research focuses on automated planning, temporal reasoning, KR, machine learning, and, more recently, also on neuro-symbolic AI, applied NLP, AI in healthcare and industry.  During his 30 years of research in AI planning, he proposed many algorithms, heuristics, languages, compilers, and architectures for several approaches to planning. With his research team he developed many planning systems, five of which awarded by ICAPS - the main conference on Automated Planning. In 2019 he also received an influential-paper award from ICAPS for a paper on planning through stochastic local search and graphs. He served the AI community in various ways, including as an editor of the Artificial Intelligence Journal. He is a fellow of EurAI and AAIA.

Abstract: AI planning requires a model of the domain actions and of the possible domain states. Given an initial state and a goal, a classical planning system uses the domain model to automatically generate, through search, a sequence of executable actions reaching the goal from the initial state.  While current techniques for classical planning are very effective, this approach has several limits, including issues related to the domain modeling. First, it can be hard or impossible to express knowledge that is not limited to single actions or single states.This can be important to characterize the desired solution plans, and is useful to provide control knowledge to the planning search in a declarative manner. Secondly, the available knowledge for modeling the domain actions and states might be inadequate, incorrect, or difficult to specify for a human.
Two research avenues for addressing these issues are: (a) Extending the simple action/state model of classical planning with additional knowledge over sequences of actions/states specified by temporal logics (e.g., PDDL3, LTL, PPLTL) that can be automatically encoded into an equivalent revised classical domain model;  (b) Exploiting machine learning to automatically acquire the domain model from data/experience, learn search heuristics, or even generate planning solutions without using any action model. In this talk, I will present some recent results in these directions.

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The Spotlight Seminars on AI Committee, 

Giuseppe De Giacomo

Chiara Ghidini

Gianluigi Greco

Marco Maratea
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