Partial evaluation is a symbolic manipulation technique used to produce efficient algorithms when part of the input to the algorithm is known. Other applications of partial evaluators such as universal compilation and...
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Partial evaluation is a symbolic manipulation technique used to produce efficient algorithms when part of the input to the algorithm is known. Other applications of partial evaluators such as universal compilation and compiler generation are also known to be possible. A partial evaluator receives as input a program and partially known input to that program, and outputs a residual program which should run at least as efficient as the input program with restricted input. In this paper we study the case where both the input and residual programs are logic programs, being the partial evaluator itself a logic program. Up to now, partial evaluators have failed to process large ''non-toy'' examples. Here we present extensions to partial evaluators which will allow us to produce more efficient residual programs using less computing resources during partial evaluation. First, the introduced extensions allow the processing of large examples, which is not possible with the previous techniques. This is now possible since the extensions use less CPU time and memory consumption during the partial evaluation process. Second, the extended partial evaluator produces smaller residual programs, producing important CPU time optimizing effects. With the standard techniques, a partial evaluator will most probably act as a pessimizer, not as an optimizer. Examples are given.
This paper provides a survey of the state of the art in nonmonotonic logic programming. In particular, we survey advances in the declarative semantics of logic programs, in query processing procedures for nonmonotonic...
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This paper provides a survey of the state of the art in nonmonotonic logic programming. In particular, we survey advances in the declarative semantics of logic programs, in query processing procedures for nonmonotonic logic programs, and in recent extensions of the nonmonotonic logic programming paradigm.
Assumption-Based Argumentation (ABA) has been shown to subsume various other non-monotonic reasoning formalisms, among them normal logic programming (LP). We re-examine the relationship between ABA and LP and show tha...
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Assumption-Based Argumentation (ABA) has been shown to subsume various other non-monotonic reasoning formalisms, among them normal logic programming (LP). We re-examine the relationship between ABA and LP and show that normal LP also subsumes (flat) ABA. More precisely, we specify a procedure that given a (flat) ABA framework yields an associated logic program with almost the same syntax whose semantics coincide with those of the ABA framework. That is, the 3-valued stable (respectively well-founded, regular, 2-valued stable, and ideal) models of the associated logic program coincide with the complete (respectively grounded, preferred, stable, and ideal) assumption labellings and extensions of the ABA framework. Moreover, we show how our results on the translation from ABA to LP can be reapplied for a reverse translation from LP to ABA, and observe that some of the existing results in the literature are in fact special cases of our work. Overall, we show that (flat) ABA frameworks can be seen as normal logic programs with a slightly different syntax. This implies that methods developed for one of these formalisms can be equivalently applied to the other by simply modifying the syntax.
Regulations are pervasive in information systems. They manifest themselves as design rules, integrity constraints, deadlines, conventions, information disclosure requirements, policies, procedures, contracts, taxes, q...
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Regulations are pervasive in information systems. They manifest themselves as design rules, integrity constraints, deadlines, conventions, information disclosure requirements, policies, procedures, contracts, taxes, quotas and other statutes. Managing regulations is difficult. Regulations are complex, change frequently and rest on models of the real world that involve unusual vocabulary if not unusual concepts. Consequently, checking compliance with regulations is tedious and error-prone. logic programming appears to provide a good framework for developing regulation management systems. Besides permitting arbitrary regulations to be modelled, it offers rapidity and ease of development, readability, incremental modifiability, extensibility and portability. These features are not provided by existing DP programming tools, database managers or conventional expert-system shells. This paper investigates the application of logic programming in a significant regulation management application: Workers' Compensation Insurance premium auditing. The insurance premium computation rules for the State of California were encoded as a large Prolog program. This application illustrates specific strengths and weaknesses of logic programming and Prolog in dealing with large-scale real-world regulations.
When integrating data coming from multiple different sources we are faced with the possibility of inconsistency in databases. A paraconsistent approach for knowledge base integration allows keeping inconsistent inform...
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When integrating data coming from multiple different sources we are faced with the possibility of inconsistency in databases. A paraconsistent approach for knowledge base integration allows keeping inconsistent information and reasoning in its presence. In this paper, we use a paraconsistent logic (LFI1) as the underlying logic for the specification of P-Datalog, a deductive query language for databases containing inconsistent information. We present a declarative semantics which captures the desired meaning of a recursive query executed over a database containing inconsistent facts and whose rules allow inferring information from inconsistent premises. We also present a bottom-up evaluation method for P-Datalog programs based on an alternating fixpoint operator. (C) 2006 Elsevier Inc. All rights reserved.
Action planning deals with the problem of finding a sequence of actions transferring the world from a given state to a desired (goal) state. This problem is important in various areas such as robotics, manufacturing, ...
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Action planning deals with the problem of finding a sequence of actions transferring the world from a given state to a desired (goal) state. This problem is important in various areas such as robotics, manufacturing, transportation, autonomic computing, computer games, etc. Action planning is a form of a reachability problem in a huge state space so it is critical to efficiently represent world states and actions (transitions between states). In this paper we present a modeling framework for planning problems based on tabled logic programming that exploits a planner module in the Picat language. In particular, we suggest techniques for structured representation of states and for including control knowledge in the description of actions. We demonstrate these techniques using the complex planning domain Cave Diving from the International Planning Competition. Experimentally, we show properties of the model for different search approaches and we compare the performance of the proposed approach with state-of-the-art automated planners. The focus of this paper is on providing guidelines for manual modeling of planning domains rather than on automated reformulation of models. (C) 2017 Elsevier B.V. All rights reserved.
An information agent is viewed as a deductive database consisting of three parts: (.) an observation database containing the facts the agent has observed or sensed from its surrounding environment;(.) an input databas...
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An information agent is viewed as a deductive database consisting of three parts: (.) an observation database containing the facts the agent has observed or sensed from its surrounding environment;(.) an input database containing the information the agent has obtained from other agents;(.) an intensional database which is a set of rules for computing derived information from the information stored in the observation and input databases. Stabilization of a system of information agents represents a capability of the agents to eventually get correct information about their Surrounding despite unpredictable environment changes and the incapability of many agents to sense Such changes causing them to have temporary incorrect information. We argue that the stabilization of a system of cooperative information agents could be understood as the convergence of the behavior of the whole system toward the behavior of a "superagent", who has the sensing and Computing capabilities of all agents combined. We show that unfortunately, stabilization is not guaranteed in general, even if the agents are fully cooperative and do not hide any information from each other. We give sufficient conditions for stabilization. We discuss the consequences of our results.
The logic programming encoding of the set-theoretic graph property known as bisimulation is analyzed. This notion is of central importance in non-well-founded set theory, semantics of concurrency, model checking, and ...
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The logic programming encoding of the set-theoretic graph property known as bisimulation is analyzed. This notion is of central importance in non-well-founded set theory, semantics of concurrency, model checking, and coinductive reasoning. From a modeling point of view, it is particularly interesting since it allows two alternative high-level characterizations. We analyze the encoding style of these modelings in various dialects of logic programming. Moreover, the notion also admits a polynomial-time maximum fixpoint procedure that we implemented in Prolog. Similar graph problems which are instead NP hard or not yet perfectly classified (e.g., graph isomorphism) can inherit most from the declarative encodings presented.
This paper develops a logic programming language, GI-log, that extends answer set programming language with a new graded modality Kω where ω is an interval satisfying ω ∪ [0, 1]. The modality is used to precede a ...
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This paper develops a logic programming language, GI-log, that extends answer set programming language with a new graded modality Kω where ω is an interval satisfying ω ∪ [0, 1]. The modality is used to precede a literal in rules bodies, and thus allows for the representation of graded introspections in the presence of multiple belief sets: KωF intuitively means: it is known that the proportion of the belief sets where F is true is in the interval ω. We define the semantics of GI-log, study the relation to the languages of strong introspections, give an algorithm for computing solutions of GI-log programs, and investigate the use of GI-log for formalizing contextual reasoning, conformant planning with threshold, and modeling a graph problem.
This paper presents a new proof technique for Automated Reasoning and logic programming which based on a generalization of the original Connection Graph paradigm of Kowalski and provides a methodology for logic Progra...
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