Zobrazeno 1 - 10
of 320
pro vyhledávání: '"DE GIACOMO, Giuseppe"'
Consider an agent acting to achieve its temporal goal, but with a "trembling hand". In this case, the agent may mistakenly instruct, with a certain (typically small) probability, actions that are not intended due to faults or imprecision in its actio
Externí odkaz:
http://arxiv.org/abs/2404.16163
We study best-effort strategies (aka plans) in fully observable nondeterministic domains (FOND) for goals expressed in Linear Temporal Logic on Finite Traces (LTLf). The notion of best-effort strategy has been introduced to also deal with the scenari
Externí odkaz:
http://arxiv.org/abs/2308.15188
Autor:
Aminof, Benjamin, De Giacomo, Giuseppe, Di Stasio, Antonio, Francon, Hugo, Rubin, Sasha, Zhu, Shufang
In this paper, we study LTLf synthesis under environment specifications for arbitrary reachability and safety properties. We consider both kinds of properties for both agent tasks and environment specifications, providing a complete landscape of synt
Externí odkaz:
http://arxiv.org/abs/2308.15184
We consider an agent acting to fulfil tasks in a nondeterministic environment. When a strategy that fulfills the task regardless of how the environment acts does not exist, the agent should at least avoid adopting strategies that prevent from fulfill
Externí odkaz:
http://arxiv.org/abs/2308.15178
Goal Recognition is the task of discerning the correct intended goal that an agent aims to achieve, given a set of goal hypotheses, a domain model, and a sequence of observations (i.e., a sample of the plan executed in the environment). Existing appr
Externí odkaz:
http://arxiv.org/abs/2306.08680
We develop a general framework for abstracting the behavior of an agent that operates in a nondeterministic domain, i.e., where the agent does not control the outcome of the nondeterministic actions, based on the nondeterministic situation calculus a
Externí odkaz:
http://arxiv.org/abs/2305.14222
One major limitation to the applicability of Reinforcement Learning (RL) to many practical domains is the large number of samples required to learn an optimal policy. To address this problem and improve learning efficiency, we consider a linear hiera
Externí odkaz:
http://arxiv.org/abs/2303.00516
Autor:
Zhu, Shufang, De Giacomo, Giuseppe
Publikováno v:
International Conference on Principles of Knowledge Representation and Reasoning (KR), 2022
Most of the synthesis literature has focused on studying how to synthesize a strategy to fulfill a task. This task is a duty for the agent. In this paper, we argue that intelligent agents should also be equipped with rights, that is, tasks that the a
Externí odkaz:
http://arxiv.org/abs/2302.03384
Every automaton can be decomposed into a cascade of basic prime automata. This is the Prime Decomposition Theorem by Krohn and Rhodes. Guided by this theory, we propose automata cascades as a structured, modular, way to describe automata as complex s
Externí odkaz:
http://arxiv.org/abs/2211.14028
Devising a strategy to make a system mimicking behaviors from another system is a problem that naturally arises in many areas of Computer Science. In this work, we interpret this problem in the context of intelligent agents, from the perspective of L
Externí odkaz:
http://arxiv.org/abs/2205.09201