Option Discovery for Autonomous Generation of Symbolic Knowledge

Autor: Gabriele Sartor, Davide Zollo, Marta Cialdea Mayer, Angelo Oddi, Riccardo Rasconi, Vieri Giuliano Santucci

Publikováno v: AIxIA 2021 – Advances in Artificial Intelligence ISBN: 9783031084201

In this work we present an empirical study where we demonstrate the possibility of developing an artificial agent that is capable to autonomously explore an experimental scenario. During the exploration, the agent is able to discover and learn intere

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0e53ee975ee3afadf7d54124efa14c6d
http://arxiv.org/abs/2206.01815

A Language for Timeline-based Planning

Autor: Giulio Bernardi, Amedeo Cesta, Andrea Orlandini, Alessandro Umbrico, Marta Cialdea Mayer

In order to represent planning problems, Artificial Intelligence planning languagesare used to describe environment’s conditions and operators which can leadto desired goals by generating a chain of actions based on these conditions andoperators. H

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dc4699cbb35c9dc0b2fcb0aca0f704f7

On timeline-based games and their complexity

Autor: Marta Cialdea Mayer, Mark Reynolds, Andrea Orlandini, Nicola Gigante, Angelo Montanari

Publikováno v: Theoretical computer science 815 (2020): 247–269. doi:10.1016/j.tcs.2020.02.011
info:cnr-pdr/source/autori:Gigante, Nicola; Montanari, Angelo; Orlandini, Andrea; Mayer, Marta Cialdea; Reynolds, Mark/titolo:On timeline-based games and their complexity/doi:10.1016%2Fj.tcs.2020.02.011/rivista:Theoretical computer science/anno:2020/pagina_da:247/pagina_a:269/intervallo_pagine:247–269/volume:815
Theoretical Computer Science

In timeline-based planning, domains are described as sets of independent, but interacting, components, whose behaviour over time (the set of timelines) is governed by a set of temporal constraints. A distinguishing feature of timeline-based planning

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::39bc286a3a73abfba8289071b0a261a4
http://www.cnr.it/prodotto/i/421808

Evaluating Robustness of an Acting Framework over Temporally Uncertain Domains

Autor: Alessandro Umbrico, Andrea Orlandini, Marta Cialdea Mayer, Amedeo Cesta

Publikováno v: Lecture Notes in Computer Science ISBN: 9783030351656
AI*IA

The ultimate goal of automated planning is the execution of plans by an artificial agent in the environment. When interactions and collaboration with humans are considered, robust plan execution requires even more highly flexible and adaptable contro

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1a89f7ba87b982bf7a28a4f2c8014a04
https://doi.org/10.1007/978-3-030-35166-3_18

Steps in Assessing a Timeline-Based Planner

Autor: Marta Cialdea Mayer, Andrea Orlandini, Alessandro Umbrico, Amedeo Cesta

Publikováno v: AI*IA 2016 Advances in Artificial Intelligence ISBN: 9783319491295
AI*IA
AI*IA 2016 Advances in Artificial Intelligence, pp. 508–522, 28/11/2016, 01/12/2016
info:cnr-pdr/source/autori:Alessandro Umbrico, Amedeo Cesta, Marta Cialdea Mayer, Andrea Orlandini/congresso_nome:AI*IA 2016 Advances in Artificial Intelligence/congresso_luogo:/congresso_data:28%2F11%2F2016, 01%2F12%2F2016/anno:2016/pagina_da:508/pagina_a:522/intervallo_pagine:508–522

The "timeline-based" is a particular paradigm of temporal planning that has been successfully applied in many real-world scenarios. Different timeline-based planning systems have been developed, each using its own planning specification language and

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5dfe5c64748409e305a2d2d678a4194c
https://doi.org/10.1007/978-3-319-49130-1_37