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04b Atto di convegno in volume
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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 hierarchy of abstraction layers of the Markov Decision...
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In this paper, we present a comparative analysis of the leading rule- based information extraction systems in both research and industry, focusing on their main characteristics and their performance. Our evaluation was performed on a dataset of text documents about financial product descriptions...
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Linear Temporal Logic (LTL) is widely used to specify temporal relationships and dynamic constraints for autonomous agents. However, in order to be used in practice in real-world domains, this high-level knowledge must be grounded in the task domain and integrated with perception and learning...
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This is a demonstration of our newly released Python package NL2LTL which leverages the latest in natural language understanding (NLU) and large language models (LLMs) to translate natural language instructions to linear temporal logic (LTL) formulas. This allows direct translation to formal...
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We study classical planning for temporally extended goals expressed in Pure-Past Linear Temporal Logic (PPLTL). PPLTL is as expressive as Linear-time Temporal Logic on finite traces (LTLf), but as shown in this paper, it is computationally much better behaved for planning. Specifically, we show...
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This extended abstract summarizes our recent work in which we study a dynamic Controlled Query Evaluation method over Description Logic ontologies.
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Behavioural Cloning is a Machine Learning method concerning how a machine attempts to autonomously mimic the actions of a human, or in general a complex controller, performing a given task. This work innovatively exploits Behavioural Cloning in support of Pediatric Neurorehabilitation. In...
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Sequential Recommender Systems (SRSs) are a popular type of recommender system that leverages user history to predict the next item of interest. However, the presence of noise in user interactions, stemming from account sharing, inconsistent preferences, or accidental clicks, can significantly...