Research in artificial intelligence and law goes back approximately 40 years. It remains largely based on formal logic, including non-monotonic logic, case-based reasoning, and logicprogramming. However, some researc...
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Research in artificial intelligence and law goes back approximately 40 years. It remains largely based on formal logic, including non-monotonic logic, case-based reasoning, and logicprogramming. However, some researchers in and practitioners of law have argued in favor of quantitative approaches (e.g. probability) to account for uncertainties in legal arguments. Other researchers have pointed some of the shortcomings of the current artificial intelligence and law research, e.g. inability to take context into account. At the same time, machine learning has made huge inroads in many different fields and applications, and therefore, the question is whether machine learning has anything to offer to the theory, and, equally important, the practice of law. As a position paper, this is a preliminary study towards the exploration of a synergistic integration of current artificial intelligence approaches in law, with machine learning approaches. It puts forward the idea that formal, logic-based approaches, currently very popular the Artificial Intelligence&Law research, could benefit from an extension with a machine learning component, and discusses some ways in which machine learning could be integrated into these approaches.
We investigate the power of non-determinism in purely functional programming languages with higher-order types. Specifically, we consider cons-free programs of varying data orders, equipped with explicit non-determini...
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ISBN:
(纸本)9783662544341;9783662544334
We investigate the power of non-determinism in purely functional programming languages with higher-order types. Specifically, we consider cons-free programs of varying data orders, equipped with explicit non-deterministic choice. Cons-freeness roughly means that data constructors cannot occur in function bodies and all manipulation of storage space thus has to happen indirectly using the call stack. While cons-free programs have previously been used by several authors to characterise complexity classes, the work on non-deterministic programs has almost exclusively considered programs of data order 0. Previous work has shown that adding explicit non-determinism to cons-free programs taking data of order 0 does not increase expressivity;we prove that this-dramatically-is not the case for higher data orders: adding non-determinism to programs with data order at least 1 allows for a characterisation of the entire class of elementary-time decidable sets. Finally we show how, even with non-deterministic choice, the original hierarchy of characterisations is restored by imposing different restrictions.
In recent work we defined resource-based answer set semantics, which is an extension to answer set semantics stemming from the study of its relationship with linear logic. In fact, the name of the new semantics comes ...
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In recent work we defined resource-based answer set semantics, which is an extension to answer set semantics stemming from the study of its relationship with linear logic. In fact, the name of the new semantics comes from the fact that in the linear-logic formulation every literal (including negative ones) were considered as a resource. In this paper, we propose a query-answering procedure reminiscent of Prolog for answer set programs under this extended semantics as an extension of XSB-resolution for logic programs with negation.(1) We prove formal properties of the proposed procedure. Under consideration for acceptance in TPLP.
The main track of the Thirty Second International Conference on logicprogramming (ICLP) took place in New York City, USA, from the 18th to the 21st October 2016. It seems fitting to hold a significant, power of two, ...
The main track of the Thirty Second International Conference on logicprogramming (ICLP) took place in New York City, USA, from the 18th to the 21st October 2016. It seems fitting to hold a significant, power of two, ICLP in New York because the city has a long and distinguished association with logicprogramming: XSB was developed at Stony Brook, as was HiLog before that, and SB-Prolog before that. Moreover, Picat was developed at the City University of New York, as was B-Prolog, and other logicprogramming-based systems, such as Ergo. New York has also been (and is) the cradle of several start-ups based on logicprogramming.
Multi-Objective Optimization in theory and practice is a simplified two-part approach to multi-objective optimization (MOO) problems. The first book presents the use of classical methods and preference-based technique...
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ISBN:
(数字)9781681085685
ISBN:
(纸本)9781681085692
Multi-Objective Optimization in theory and practice is a simplified two-part approach to multi-objective optimization (MOO) problems. The first book presents the use of classical methods and preference-based techniques. The book explains classical methods for solving MOO problems through nine chapters. Topics covered in this part are the design of current MOO problems, the complexity of MOO problems with nonlinearities and uncertainties, the theory of Pareto optimality, the introductory problem solving methods (including Zeleny’s simplex method), preference-based methods, structures of MOO problems (such as the mixed-integer programming, hierarchical optimization, fuzzy logicprogramming and bimatrix games). Multi-Objective Optimization in theory and practice is a user-friendly book with detailed, illustrated calculations, examples, test functions, and small-size applications in Mathematica® (among other packages) and from scholarly literature. It is an essential handbook for students and teachers involved in advanced optimization courses in engineering, information science and mathematics degree programs.
The paper introduces a new modular action language, ALM, and illustrates the methodology of its use. It is based on the approach of Gelfond and Lifschitz (1993, Journal of logicprogramming 17, 2-4, 301-321;1998, Elec...
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The paper introduces a new modular action language, ALM, and illustrates the methodology of its use. It is based on the approach of Gelfond and Lifschitz (1993, Journal of logicprogramming 17, 2-4, 301-321;1998, Electronic Transactions on AI 3, 16, 193-210) in which a high-level action language is used as a front end for a logicprogramming system description. The resulting logicprogramming representation is used to perform various computational tasks. The methodology based on existing action languages works well for small and even medium size systems, but is not meant to deal with larger systems that require structuring of knowledge. ALM is meant to remedy this problem. Structuring of knowledge in ALM is supported by the concepts of module (a formal description of a specific piece of knowledge packaged as a unit), module hierarchy, and library, and by the division of a system description of ALM into two parts: theory and structure. A theory consists of one or more modules with a common theme, possibly organized into a module hierarchy based on a dependency relation. It contains declarations of sorts, attributes, and properties of the domain together with axioms describing them. Structures are used to describe the domain's objects. These features, together with the means for defining classes of a domain as special cases of previously defined ones, facilitate the stepwise development, testing, and readability of a knowledge base, as well as the creation of knowledge representation libraries.
An answer set is a plain set of literals which has no further structure that would explain why certain literals are part of it and why others are not. We show how argumentation theory can help to explain why a literal...
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An answer set is a plain set of literals which has no further structure that would explain why certain literals are part of it and why others are not. We show how argumentation theory can help to explain why a literal is or is not contained in a given answer set by defining two justification methods, both of which make use of the correspondence between answer sets of a logic program and stable extensions of the assumption-based argumentation (ABA) framework constructed from the same logic program. Attack Trees justify a literal in argumentation-theoretic terms, i.e. using arguments and attacks between them, whereas ABA-Based Answer Set Justifications express the same justification structure in logicprogramming terms, that is using literals and their relationships. Interestingly, an ABA-Based Answer Set Justification corresponds to an admissible fragment of the answer set in question, and an Attack Tree corresponds to an admissible fragment of the stable extension corresponding to this answer set.
CLP(H) is an instantiation of the general constraint logicprogramming scheme with the constraint domain of hedges. Hedges are finite sequences of unranked terms, built over variadic function symbols and three kinds o...
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CLP(H) is an instantiation of the general constraint logicprogramming scheme with the constraint domain of hedges. Hedges are finite sequences of unranked terms, built over variadic function symbols and three kinds of variables: for terms, for hedges, and for function symbols. Constraints involve equations between unranked terms and atoms for regular hedge language membership. We study algebraic semantics of CLP(H) programs, define a sound, terminating, and incomplete constraint solver, investigate two fragments of constraints for which the solver returns a complete set of solutions, and describe classes of programs that generate such constraints.
In previous work, we proposed a logic-based framework in which computation is the execution of actions in an attempt to make reactive rules of the form if antecedent then consequent true in a canonical model of a logi...
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In previous work, we proposed a logic-based framework in which computation is the execution of actions in an attempt to make reactive rules of the form if antecedent then consequent true in a canonical model of a logic program determined by an initial state, sequence of events, and the resulting sequence of subsequent states. In this model-theoretic semantics, reactive rules are the driving force, and logic programs play only a supporting role. In the canonical model, states, actions, and other events are represented with timestamps. But in the operational semantics (OS), for the sake of efficiency, timestamps are omitted and only the current state is maintained. State transitions are performed reactively by executing actions to make the consequents of rules true whenever the antecedents become true. This OS is sound, but incomplete. It cannot make reactive rules true by preventing their antecedents from becoming true, or by proactively making their consequents true before their antecedents become true. In this paper, we characterize the notion of reactive model, and prove that the OS can generate all and only such models. In order to focus on the main issues, we omit the logicprogramming component of the framework.
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