Justifications for Logic Programs Under Answer Set Semantics

Pontelli, Enrico; Son, Tran Cao

doi:10.1007/11799573_16

Cited by 38 publications

(73 citation statements)

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“…Profiling may thus be used on correctly executing programs, and does not address the question of why given solutions are returned or omitted. For this reason, profiling differs from previously reported approaches based on procedural or declarative debugging or on justification (e.g., [9,16]).…”

Section: Introductionmentioning

confidence: 88%

“…8. Examination of this output indicates that the SCC consists of a large number of fully ground calls to several predicates: rewriting code to make fewer but less instantiated calls to these predicates will often optimize a computation in such cases.Of course, abstract_modes/2 is simply an example: term abstraction predicates are easy to write, and any such predicate may be passed into the last argument of analyze_an_scc/316 .…”

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confidence: 99%

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Forest logging: A trace-based analysis of large rule-based computations

Swift

2015

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Knowledge representation systems based on the well-founded semantics can offer the degree of scalability required for semantic web applications and make use of expressive semantic features such as Hilog, frame-based reasoning, and defeasibility theories. Such features can be compiled into Prolog tabling engines that have good support for indexing and memory management. However, due both to the power of the semantic features and to the declarative style typical of knowledge representation rules, the resources needed for query evaluation can be unpredictable. In such a situation, users need to understand the overall structure of a computation and examine problematic portions of it. This problem, of profiling a computation, differs from debugging and justification which address why a given answer was or wasn't derived, and so profiling requires different techniques. In this paper we present a trace-based analysis technique called forest logging which has been used to profile large, heavily tabled computations. In forest logging, critical aspects of a tabled computation are logged; afterwards the log is loaded and analyzed. As implemented in XSB, forest logging slows down execution of practical programs by a constant factor that is often small; and logs containing tens or hundreds of millions of facts can be loaded and analyzed in minutes.

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Section: Introductionmentioning

confidence: 88%

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confidence: 99%

Forest logging: A trace-based analysis of large rule-based computations

Swift

2015

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“…All approaches reviewed here, except for the formal theory of justifications (Section 3.5.2), aim to provide concise structures called justifications that provide a somewhat minimal explanation as to why a literal in question belongs to an answer set. We start by introducing off-line (Section 3.1; Pontelli et al 2009;Pontelli and Son 2006), LABAS (Section 3.2; Schulz and Toni 2016; Schulz and Toni 2013) and causal justifications (Section 3.3; Cabalar and Fandinno 2016). In these three approaches, justifications are represented as different kinds of dependency graphs between literals and/or rules.…”

Section: Justifications Of Consistent Logic Programsmentioning

confidence: 99%

Answering the “why” in answer set programming – A survey of explanation approaches

Fandinno¹,

Schulz²

2019

Theory and Practice of Logic Programming

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Artificial intelligence (AI) approaches to problem-solving and decision-making are becoming more and more complex, leading to a decrease in the understandability of solutions. The European Union’s new General Data Protection Regulation tries to tackle this problem by stipulating a “right to explanation” for decisions made by AI systems. One of the AI paradigms that may be affected by this new regulation is answer set programming (ASP). Thanks to the emergence of efficient solvers, ASP has recently been used for problem-solving in a variety of domains, including medicine, cryptography, and biology. To ensure the successful application of ASP as a problem-solving paradigm in the future, explanations of ASP solutions are crucial. In this survey, we give an overview of approaches that provide an answer to the question ofwhyan answer set is a solution to a given problem, notably off-line justifications, causal graphs, argumentative explanations, and why-not provenance, and highlight their similarities and differences. Moreover, we review methods explaining why a set of literals isnotan answer set or why no solution exists at all.

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“…For instance, classical debugging techniques do often not apply to ASP because of its high degree of declarativity, or in other words, its lack of a procedural semantics that could be subject to debugging and tracing. This "curse of declarativity" is well recognized withing the ASP community and addressed within a dedicated workshop series [21,22]; first approaches can be found in [23][24][25][26].…”

Section: Asp Solvingmentioning

confidence: 99%

Challenges in Answer Set Solving

Gebser

Kaminski

Kaufmann

et al. 2011

Lecture Notes in Computer Science

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Michael Gelfond's application of Answer Set Programming (ASP) in the context of NASA's space shuttle has opened the door of the ivory tower. His project has not only given our community self-confidence and served us as a reference for grant agencies and neighboring fields, but ultimately it helped freeing the penguins making them exclaim "Yes, we can [fly] !". The community has taken up this wonderful assist to establish ASP as a prime tool for declarative problem solving in the area of Knowledge Representation and Reasoning. Despite this success, however, ASP has not yet attracted broad attention outside this area. This paper aims at identifying some current challenges that our field has to overcome in the mid-run to ultimately become a full-fledged technology in Informatics. Affiliated with the

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Justifications for Logic Programs Under Answer Set Semantics

Cited by 38 publications

References 10 publications

Forest logging: A trace-based analysis of large rule-based computations

Forest logging: A trace-based analysis of large rule-based computations

Answering the “why” in answer set programming – A survey of explanation approaches

Challenges in Answer Set Solving

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