906 research outputs found

    Modular Logic Programming: Full Compositionality and Conflict Handling for Practical Reasoning

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    With the recent development of a new ubiquitous nature of data and the profusity of available knowledge, there is nowadays the need to reason from multiple sources of often incomplete and uncertain knowledge. Our goal was to provide a way to combine declarative knowledge bases – represented as logic programming modules under the answer set semantics – as well as the individual results one already inferred from them, without having to recalculate the results for their composition and without having to explicitly know the original logic programming encodings that produced such results. This posed us many challenges such as how to deal with fundamental problems of modular frameworks for logic programming, namely how to define a general compositional semantics that allows us to compose unrestricted modules. Building upon existing logic programming approaches, we devised a framework capable of composing generic logic programming modules while preserving the crucial property of compositionality, which informally means that the combination of models of individual modules are the models of the union of modules. We are also still able to reason in the presence of knowledge containing incoherencies, which is informally characterised by a logic program that does not have an answer set due to cyclic dependencies of an atom from its default negation. In this thesis we also discuss how the same approach can be extended to deal with probabilistic knowledge in a modular and compositional way. We depart from the Modular Logic Programming approach in Oikarinen & Janhunen (2008); Janhunen et al. (2009) which achieved a restricted form of compositionality of answer set programming modules. We aim at generalising this framework of modular logic programming and start by lifting restrictive conditions that were originally imposed, and use alternative ways of combining these (so called by us) Generalised Modular Logic Programs. We then deal with conflicts arising in generalised modular logic programming and provide modular justifications and debugging for the generalised modular logic programming setting, where justification models answer the question: Why is a given interpretation indeed an Answer Set? and Debugging models answer the question: Why is a given interpretation not an Answer Set? In summary, our research deals with the problematic of formally devising a generic modular logic programming framework, providing: operators for combining arbitrary modular logic programs together with a compositional semantics; We characterise conflicts that occur when composing access control policies, which are generalisable to our context of generalised modular logic programming, and ways of dealing with them syntactically: provided a unification for justification and debugging of logic programs; and semantically: provide a new semantics capable of dealing with incoherences. We also provide an extension of modular logic programming to a probabilistic setting. These goals are already covered with published work. A prototypical tool implementing the unification of justifications and debugging is available for download from http://cptkirk.sourceforge.net

    A Brief History of Updates of Answer-Set Programs

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    Funding Information: The authors would like to thank José Alferes, Martin Baláz, Federico Banti, Antonio Brogi, Martin Homola, Luís Moniz Pereira, Halina Przymusinska, Teodor C. Przymusinski, and Theresa Swift, with whom they worked on the topic of this paper over the years, as well as Ricardo Gonçalves and Matthias Knorr for valuable comments on an earlier draft of this paper. The authors would also like to thank the anonymous reviewers for their insightful comments and suggestions, which greatly helped us improve this paper. The authors were partially supported by Fundação para a Ciência e Tecnologia through projects FORGET (PTDC/CCI-INF/32219/2017) and RIVER (PTDC/CCI-COM/30952/2017), and strategic project NOVA LINCS (UIDB/04516/2020). Publisher Copyright: © The Author(s), 2022. Published by Cambridge University Press.Over the last couple of decades, there has been a considerable effort devoted to the problem of updating logic programs under the stable model semantics (a.k.a. answer-set programs) or, in other words, the problem of characterising the result of bringing up-to-date a logic program when the world it describes changes. Whereas the state-of-the-art approaches are guided by the same basic intuitions and aspirations as belief updates in the context of classical logic, they build upon fundamentally different principles and methods, which have prevented a unifying framework that could embrace both belief and rule updates. In this paper, we will overview some of the main approaches and results related to answer-set programming updates, while pointing out some of the main challenges that research in this topic has faced.publishersversionpublishe

    EMIL: Extracting Meaning from Inconsistent Language

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    Developments in formal and computational theories of argumentation reason with inconsistency. Developments in Computational Linguistics extract arguments from large textual corpora. Both developments head in the direction of automated processing and reasoning with inconsistent, linguistic knowledge so as to explain and justify arguments in a humanly accessible form. Yet, there is a gap between the coarse-grained, semi-structured knowledge-bases of computational theories of argumentation and fine-grained, highly-structured inferences from knowledge-bases derived from natural language. We identify several subproblems which must be addressed in order to bridge the gap. We provide a direct semantics for argumentation. It has attractive properties in terms of expressivity and complexity, enables reasoning by cases, and can be more highly structured. For language processing, we work with an existing controlled natural language (CNL), which interfaces with our computational theory of argumentation; the tool processes natural language input, translates them into a form for automated inference engines, outputs argument extensions, then generates natural language statements. The key novel adaptation incorporates the defeasible expression ‘it is usual that’. This is an important, albeit incremental, step to incorporate linguistic expressions of defeasibility. Overall, the novel contribution of the paper is an integrated, end-to-end argumentation system which bridges between automated defeasible reasoning and a natural language interface. Specific novel contributions are the theory of ‘direct semantics’, motivations for our theory, results with respect to the direct semantics, an implementation, experimental results, the tie between the formalisation and the CNL, the introduction into a CNL of a natural language expression of defeasibility, and an ‘engineering’ approach to fine-grained argument analysis

    Explanation Generation for Multi-Modal Multi-Agent Path Finding with Optimal Resource Utilization using Answer Set Programming

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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. This paper is under consideration for acceptance in TPLP.Comment: Paper presented at the 36th International Conference on Logic Programming (ICLP 2020), University Of Calabria, Rende (CS), Italy, September 2020, 16 pages, 6 figure

    Test Programming by Program Composition and Symbolic Simulation

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    Classical test generation techniques rely on search through gate-level circuit descriptions, which results in long runtimes. In some instances, classical techniques cannot be used because they would take longer than the lifetime of the product to generate tests which are needed when the first devices come off the assembly line. Despite these difficulties, human experts often succeed in writing test programs for very complex circuits. How can we account for their success? We take a knowledge engineering approach to this problem by trying to capture in a program techniques gleaned from working with experienced test programmers. From these talks, we conjecture that expert test programming performance relies in part on two aspects of human problem solving. First, the experts remember many cliched solutions to test programming problems. The difficulty lies in formalizing the notion of a cliche for this domain. For test programming, we propose that cliches contain goal to subgoal expansions, fragments of test program code, and constraints describing how program fragments fit together. We present an algorithm which uses testing cliches to generate test programs. Second, experts can simulate a circuit at various levels of abstraction and recognize patterns of activity in the circuit which are useful for solving test problems. We argue that symbolic simulation coupled with recognition of which simulated events solve our goals is an effective planning strategy in certain cases. We present a second algorithm which simulates circuit behavior on symbolic inputs at roughly the register transfer level and generates fragments of test programs suitable for use by our first algorithm.MIT Artificial Intelligence Laborator

    A Temporal extension of Prolog

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    AbstractTemporal Prolog, a temporal logic extension of PROLOG, is presented. The primary criterion for the model selection has been its natural embedment into the logic programming paradigm. Under strong efficiency constraints, a first-order “reified” logic has been taken as a basis for the implementation. Allen's temporal constraint algorithm has been extended for treatment of retractable constraints. Their embedment into Temporal Prolog can be viewed as an instance of the Constraint Logic Programming paradigm. An example inspired by K. Forbus's Qualitative Process Theory illustrates how qualitative simulation and related tasks can be formulated in Temporal Prolog in a transparent and declarative way
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