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pro vyhledávání: '"Baharisangari, Nasim"'
Autor:
Paliwal, Yash, Roy, Rajarshi, Gaglione, Jean-Raphaël, Baharisangari, Nasim, Neider, Daniel, Duan, Xiaoming, Topcu, Ufuk, Xu, Zhe
We study a class of reinforcement learning (RL) tasks where the objective of the agent is to accomplish temporally extended goals. In this setting, a common approach is to represent the tasks as deterministic finite automata (DFA) and integrate them
Externí odkaz:
http://arxiv.org/abs/2306.13732
This paper addresses the problem of data-driven model discrimination for unknown switched systems with unknown linear temporal logic (LTL) specifications, representing tasks, that govern their mode sequences, where only sampled data of the unknown dy
Externí odkaz:
http://arxiv.org/abs/2306.09966
Autor:
Gaglione, Jean-Raphaël, Roy, Rajarshi, Baharisangari, Nasim, Neider, Daniel, Xu, Zhe, Topcu, Ufuk
Learning linear temporal logic (LTL) formulas from examples labeled as positive or negative has found applications in inferring descriptions of system behavior. We summarize two methods to learn LTL formulas from examples in two different problem set
Externí odkaz:
http://arxiv.org/abs/2212.00916
Autor:
Baharisangari, Nasim, Xu, Zhe
In this paper, we propose a distributed differentially private receding horizon control (RHC) approach for multi-agent systems (MAS) with metric temporal logic (MTL) specifications. In the MAS considered in this paper, each agent privatizes its sensi
Externí odkaz:
http://arxiv.org/abs/2210.01759
Autor:
Roy, Rajarshi, Gaglione, Jean-Raphaël, Baharisangari, Nasim, Neider, Daniel, Xu, Zhe, Topcu, Ufuk
We consider the problem of explaining the temporal behavior of black-box systems using human-interpretable models. To this end, based on recent research trends, we rely on the fundamental yet interpretable models of deterministic finite automata (DFA
Externí odkaz:
http://arxiv.org/abs/2209.02650
Extracting spatial-temporal knowledge from data is useful in many applications. It is important that the obtained knowledge is human-interpretable and amenable to formal analysis. In this paper, we propose a method that trains neural networks to lear
Externí odkaz:
http://arxiv.org/abs/2109.08078
Temporal logic inference is the process of extracting formal descriptions of system behaviors from data in the form of temporal logic formulas. The existing temporal logic inference methods mostly neglect uncertainties in the data, which results in l
Externí odkaz:
http://arxiv.org/abs/2105.11545
Publikováno v:
In IFAC PapersOnLine 2023 56(2):11261-11266
Akademický článek
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