State-of-the-art answer set programming (ASP) solvers rely on a program called a grounder to convert non-ground programs containing variables into variable-free, propositional programs. The size of this grounding depe...
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State-of-the-art answer set programming (ASP) solvers rely on a program called a grounder to convert non-ground programs containing variables into variable-free, propositional programs. The size of this grounding depends heavily on the size of the non-ground rules, and thus, reducing the size of such rules is a promising approach to improve solving performance. To this end, in this paper we announce 1popt, a tool that decomposes large logic programming rules into smaller rules that are easier to handle for current solvers. The tool is specifically tailored to handle the standard syntax of the ASP language (ASP-Core) and makes it easier for users to write efficient and intuitive ASP programs, which would otherwise often require significant handtuning by expert ASP engineers. It is based on an idea proposed by Morak and Woltran (2012) that we extend significantly in order to handle the full ASP syntax, including complex constructs like aggregates, weak constraints, and arithmetic expressions. We present the algorithm, the theoretical foundations on how to treat these constructs, as well as an experimental evaluation showing the viability of our approach.
This paper introduces argumentation over defeasible preferences in Arg2P, an argumentation framework based on logic programming. A computational mechanism is first implemented in Arg2P according to Dung's defeasib...
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The multi-agent path finding (MAPF) problem is a combinatorial search problem that aims at finding paths for multiple agents (e.g., robots) in an environment (e.g., an autonomous warehouse) such that no two agents col...
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The multi-agent path finding (MAPF) problem is a combinatorial search problem that aims at finding paths for multiple agents (e.g., robots) in an environment (e.g., an autonomous warehouse) such that no two agents collide with each other, and subject to some constraints on the lengths of paths. We consider a general version of MAPF, called mMAPF, that involves multi-modal transportation modes (e.g., due to velocity constraints) and consumption of different types of resources (e.g., batteries). The real-world applications of mMAPF require flexibility (e.g., solving variations of mMAPF) as well as explainability. Our earlier studies on mMAPF have focused on the former challenge of flexibility. In this study, we focus on the latter challenge of explainability, and introduce a method for generating explanations for queries regarding the feasibility and optimality of solutions, the nonexistence of solutions, and the observations about solutions. Our method is based on answer set programming.
Probabilistic Answer Set programming (PASP) combines rules, facts, and independent probabilistic facts. We argue that a very useful modeling paradigm is obtained by adopting a particular semantics for PASP, where one ...
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Probabilistic Answer Set programming (PASP) combines rules, facts, and independent probabilistic facts. We argue that a very useful modeling paradigm is obtained by adopting a particular semantics for PASP, where one associates a credal set with each consistent program. We examine the basic properties of PASP under this credal semantics, in particular presenting novel results on its complexity and its expressivity, and we introduce an inference algorithm to compute (upper) probabilities given a program. (C) 2020 Elsevier Inc. All rights reserved.
In this paper we investigate the shift from two-valued to many-valued logic programming, including extensions involving functorial and monadic constructions for sentences building upon terms. We will show that assigni...
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Research on developing efficient and scalable ASP solvers can substantially benefit by the availability of data sets to experiment with. KB Bio 101 contains knowledge from a biology textbook, has been developed as par...
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Reasoning in very complex contexts often requires purely deductive reasoning to be supported by a variety of techniques that can cope with incomplete data. Abductive inference allows to guess information that has not ...
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Reasoning in very complex contexts often requires purely deductive reasoning to be supported by a variety of techniques that can cope with incomplete data. Abductive inference allows to guess information that has not been explicitly observed. Since there are many explanations for such guesses, there is the need for assigning a probability to each one. This work exploits logical abduction to produce multiple explanations consistent with a given background knowledge and defines a strategy to prioritize them using their chance of being true. Another novelty is the introduction of probabilistic integrity constraints rather than hard ones. Then we propose a strategy that learns model and parameters from data and exploits our Probabilistic Abductive Proof Procedure to classify never-seen instances. This approach has been tested on some standard datasets showing that it improves accuracy in presence of corruptions and missing data.
Syntactical rule based approaches for aspect extraction, which are free from expensive manual annotation, are promising in practice. These approaches extract aspects mainly through the dependency relations in the surf...
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Syntactical rule based approaches for aspect extraction, which are free from expensive manual annotation, are promising in practice. These approaches extract aspects mainly through the dependency relations in the surface sentence structures. However, deep and rich semantic information hidden in sentences which can help improve aspect extraction, is difficult for them to capture. In order to address the problem, this paper first proposes to employ logic programming to explore the feasibility of deep semantic representation, then proposes Deep2S, a hybrid rule-based method to improve the performance of aspect extraction. Deep2S integrates deep semantic representation such as Abstract Meaning Representation (AMR) with syntactic structure. It can take advantage of the syntactical rules to obtain dependency relations in the surface structure as well as the semantic rules to capture deep semantic information. Our experiments are conducted on eight popular review datasets using two evaluation metrics. Experimental results demonstrate the usefulness of deep semantic representation and the ability of Deep2S to improve the performance of aspect extraction in opinion mining.
Understanding why and how a given answer to a query is generated from a deductive or relational database is fundamental to obtain justifications, assess trust, and detect dependencies on contradictions. Propagating pr...
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We describe the portable and efficient implementation of coinductive logic programming found in Logtalk, discussing its features and limitations. As Logtalk uses as a back-end compiler a compatible Prolog system, we a...
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