The course timetabling problem can be generally defined as the task of assigning a number of lectures to a limited set of timeslots and rooms, subject to a given set of hard and soft constraints. The modeling language...
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The course timetabling problem can be generally defined as the task of assigning a number of lectures to a limited set of timeslots and rooms, subject to a given set of hard and soft constraints. The modeling language for course timetabling is required to be expressive enough to specify a wide variety of soft constraints and objective functions. Furthermore, the resulting encoding is required to be extensible for capturing new constraints and for switching them between hard and soft, and to be flexible enough to deal with different formulations. In this paper, we propose to make effective use of ASP as a modeling language for course timetabling. We show that our ASP-based approach can naturally satisfy the above requirements, through an ASP encoding of the curriculum-based course timetabling problem proposed in the third track of the second international timetabling competition (ITC-2007). Our encoding is compact and human-readable, since each constraint is individually expressed by either one or two rules. Each hard constraint is expressed by using integrity constraints and aggregates of ASP. Each soft constraint S is expressed by rules in which the head is the form of penalty (S, V, C), and a violation V and its penalty cost C are detected and calculated respectively in the body. We carried out experiments on four different benchmark sets with five different formulations. We succeeded either in improving the bounds or producing the same bounds for many combinations of problem instances and formulations, compared with the previous best known bounds.
The ANGELIC methodology was successfully used to predict decisions of the European Court of Human Rights based on a set of logical rules, with significantly better accuracy than the one achieved by machine learning ap...
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ISBN:
(纸本)9781643684727;9781643684734
The ANGELIC methodology was successfully used to predict decisions of the European Court of Human Rights based on a set of logical rules, with significantly better accuracy than the one achieved by machine learning approaches, as well as to explain the results of reasoning, quite valuable in order to make them trustworthy. This work demonstrates a different logic-based approach, based on answer set programming for solving and generating explanations for solutions. The use of a general knowledge representation and reasoning system, where representation and inference are not tightly coupled, allows for using the same representation for inference tasks different from prediction, thus getting more value out of the domain model, and opens for integrating further forms of knowledge.
Controlled natural languages are subsets of natural languages that can be used to describe a problem in a very precise way, furthermore they can often be translated automatically into a formal notation. We investigate...
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ISBN:
(纸本)9783642326127
Controlled natural languages are subsets of natural languages that can be used to describe a problem in a very precise way, furthermore they can often be translated automatically into a formal notation. We investigate in this paper how a controlled natural language can be used as a specification language for answer set programming (ASP). ASP is a declarative approach to problem solving and has its roots in knowledge representation, logic programming, and constraint satisfaction. Solutions of ASP programs are stable models (= answersets) that build the starting point for question answering. As a proof of concept, we translate a problem specification written in controlled natural language into an ASP program and compute a stable model that contains the answers to a number of questions.
The paper presents some applications in planning and multi-agent systems of answer set programming. It highlights the benefits of answer set programming based techniques in these applications. It also describes a clas...
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ISBN:
(纸本)9783319616605;9783319616599
The paper presents some applications in planning and multi-agent systems of answer set programming. It highlights the benefits of answer set programming based techniques in these applications. It also describes a class of multi-agent planning problems that is challenging to answer set programming.
Procedural Content Generation is applied in the development process of many commercial games: automatically generated game contents are delivered to players in order to offer a constantly changing user experience and ...
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ISBN:
(纸本)9783030038403;9783030038397
Procedural Content Generation is applied in the development process of many commercial games: automatically generated game contents are delivered to players in order to offer a constantly changing user experience and enrich the game itself. Usually, the generative process relies on search-based non-deterministic algorithms, which encode one or more techniques for guaranteeing "legal" yet diversified output. Declarative approaches to content generation, more properly defined as Declarative Content Specification techniques, like the ones based on answer set programming, allow to focus on describing content requirements rather than programming ad-hoc generation engines, and to fast prototype generation techniques themselves. This work investigates to what extent ASP-based DCS is scalable enough for industrial contexts, by proposing a partitioning-based approach. A working prototype, available as an Unity Asset and as a GVGAI framework level generator is presented.
The present paper describes briefly a project idea in progress about the evolvement of individuals' opinions, beliefs and perceptions on social networks (such as Facebook, Twitter, Instagram, youtube...) which is ...
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ISBN:
(纸本)9781450389228
The present paper describes briefly a project idea in progress about the evolvement of individuals' opinions, beliefs and perceptions on social networks (such as Facebook, Twitter, Instagram, youtube...) which is a thorny subject that has whetted nowadays the curiosity of a hulk of researchers from various disciplines. For this purpose, differently from a lot of works in the literature, we rely on logical knowledge representation tools in order to investigate the belief merging operation of Artificial Intelligence (AI). The major objective of this project is to provide efficient operator for merging heterogeneous, inconsistent and uncertain multiple sources information in the context of social networks taking into account the fact that opinion can be formed and developed through the concept of social influence with its two forms (informational social influence and normative social influence) and the concept of social trust. We intend thus through this research work presenting an adaptative version to our context of an approach [7] expressed thanks to answer set programming (ASP) paradigm with stable model semantics. It is worth to say that our approach profits from the impressive volume data produced by users in social networks about a particular topic by learning from opinions, beliefs and perceptions that their freinds/neighbors share and therefore allows to use this kind of data to extract initial opinions, and to validate the proposed opinions merging process allowing even the prediction of users' behaviors.
Procedural content generators for games produce artifacts from a latent design space. This space is often only implicitly defined, an emergent result of the procedures used in the generator. In this paper, we outline ...
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Procedural content generators for games produce artifacts from a latent design space. This space is often only implicitly defined, an emergent result of the procedures used in the generator. In this paper, we outline an approach to content generation that centers on explicit description of the design space, using domain-independent procedures to produce artifacts from the described space. By concisely capturing a design space as an answerset program, we can rapidly define and expressively sculpt new generators for a variety of game content domains. We walk through the reimplementation of a reference evolutionary content generator in a tutorial example, and review existing applications of answer set programming to generative-content design problems in and outside of a game context.
We describe different ASP encodings to show the independence of some axioms of G(3)' logic, and to obtain a paraconsistent multivalued logic based on the axioms of G(3)' logic.
ISBN:
(纸本)9780769539331
We describe different ASP encodings to show the independence of some axioms of G(3)' logic, and to obtain a paraconsistent multivalued logic based on the axioms of G(3)' logic.
answer set programming (ASP) is a declarative programming paradigm targeted to solving search problems. The basic idea of ASP is similar to, for example, SAT-based planning or constraint satisfaction problems but ASP ...
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ISBN:
(纸本)9780769544052
answer set programming (ASP) is a declarative programming paradigm targeted to solving search problems. The basic idea of ASP is similar to, for example, SAT-based planning or constraint satisfaction problems but ASP provides a more powerful knowledge representation language for effective problem encoding. A number of successful ASP systems have already been developed and applied in a large range of areas. The talk explains the theoretical underpinnings of ASP, introduces the answer set programming paradigm, outlines computational techniques used in current ASP solvers, and discusses some interesting applications of the approach.
Designing and implementing AI in games is an interesting, yet complex task. This paper briefly presents some applications that make use of answer set programming for such a task, and show some advantages of declarativ...
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Designing and implementing AI in games is an interesting, yet complex task. This paper briefly presents some applications that make use of answer set programming for such a task, and show some advantages of declarative programming frameworks against imperative (algorithmic) approaches while dealing with knowledge representation and reasoning: solid theoretical bases, no need for algorithm design or coding, explicit (and thus easily modifiable/upgradeable) knowledge representation, declarative specifications which are already executable, very fast prototyping, quick error detection, modularity.
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