Conflict resolution is an important issue in knowledge representation and reasoning. A common idea of solving conflicts in reasoning is to introduce priorities in the underlying reasoning mechanism. This paper describ...
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Conflict resolution is an important issue in knowledge representation and reasoning. A common idea of solving conflicts in reasoning is to introduce priorities in the underlying reasoning mechanism. This paper describes an implementation of the propositional version of prioritized logic programs (PLPs) proposed by Zhang and Foo recently [7]. As a central part of the system development, in this paper we outline the basic structure of the system, discuss major algorithms of computing answer sets of PLPs and illustrate the system performance based on various testing samples.
Multi-adjoint logic programs generalise monotonic and residuated logic programs in that simultaneous use of several implications in the rules and rather general connectives in the bodies are allowed. As our approach h...
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Multi-adjoint logic programs generalise monotonic and residuated logic programs in that simultaneous use of several implications in the rules and rather general connectives in the bodies are allowed. As our approach has continuous fixpoint semantics, in this work, a procedural semantics is given for the paradigm of multi-adjoint logic programming and a completeness result is proved. Some applications which could benefit from this theoretical approach, such as threshold computation, fuzzy databases and general fuzzy resolution, are commented on.
We propose a robot architecture to integrate symbolic and non-symbolic information processings. Artificial neural networks (ANN) are quick, flexible and robust. Symbolic processing is on the other hand comprehensible,...
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We propose a robot architecture to integrate symbolic and non-symbolic information processings. Artificial neural networks (ANN) are quick, flexible and robust. Symbolic processing is on the other hand comprehensible, effective, controllable, and consistent. To integrate symbolic and non-symbolic methods, we consider the relation between a robot and its environment as constraints. To describe and solve such constraints we turn to constraint logic programming (CLP). To construct a robot that works in the complex environment, CLP and ANN are integrated into a unified framework such that CLP evaluates the behavior candidates proposed by ANN according to the constraints and ANN learns adequate behavior according to evaluations by CLP. We implemented the decision process in our robot that drove through a test course as we expected.
The object-oriented philosophy creates a powerful synergy throughout the development life-cycle by combining abstraction, encapsulation and modularity. However, existing object-oriented programming languages are low-l...
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The object-oriented philosophy creates a powerful synergy throughout the development life-cycle by combining abstraction, encapsulation and modularity. However, existing object-oriented programming languages are low-level procedural ones that are hard to program and debug. logic programming allows computing problems to be expressed in a high-level declarative way, without giving instructions on how the problem is to be solved. However, logic programming lacks powerful methods to model applications. In the past few decades, the integration of object-oriented programming and logic programming has attracted a lot of interest, with a number of languages being proposed, but these languages fail to address some key object-oriented features properly. In this paper, we introduce a novel object-oriented logic programming language that supports nearly all key object-oriented features in a logic programming framework. We also describe its operational semantics.
The aim of the paper is to show relationships between different formalisms for handling uncertainty in logic programming, knowledge based systems and deductive databases. Namely, we show that our model of fuzzy logic ...
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The aim of the paper is to show relationships between different formalisms for handling uncertainty in logic programming, knowledge based systems and deductive databases. Namely, we show that our model of fuzzy logic programming has the same expressive power as annotated logic programs with restricted continuous semantics. Features of fuzzy unification are achieved by extending the rule base by axioms of equality with fuzzy similarities. This induces a new fuzzy relational algebra. Our procedural semantics enables us to estimate truth values of the answers during the computation. Using this, we introduce a model with threshold computation and another for finding the best answer with prescribed precision tolerance.
This paper presents a new high-level synthesis (HLS) approach which addresses the problem of synthesis of conditional behaviors. In proposed methodology, the conditional behaviors are represented by Hierarchical Condi...
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This paper presents a new high-level synthesis (HLS) approach which addresses the problem of synthesis of conditional behaviors. In proposed methodology, the conditional behaviors are represented by Hierarchical Conditional Dependence Graphs (HCDG) and synthesized using derived Constraints logic programming (CLP) models. Our synthesis methods exploit multicycle operations and chaining as well as conditional resource sharing and speculative execution at the same time. These techniques are essential in HLS and the experiments carried out using the developed prototype system showed good performance of the synthesized designs and proved the feasibility of the presented approach.
Character recognition systems can contribute tremendously to the advancement of the automation process and can improve the interaction between man and machine in many applications, including office automation, cheque ...
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Character recognition systems can contribute tremendously to the advancement of the automation process and can improve the interaction between man and machine in many applications, including office automation, cheque verification and a large variety of banking, business and data entry applications. The main theme of this paper is the automatic recognition of hand-printed Arabic characters using machine learning. Conventional methods have relied on hand-constructed dictionaries which are tedious to construct and difficult to make tolerant to variation in writing styles. The advantages of machine learning are that it can generalize over the large degree of variation between writing styles and recognition rules can be constructed by example. The system was tested on a sample of handwritten characters from several individuals whose writing ranged from acceptable to poor in quality and the correct average recognition rate obtained using cross-validation was 86.65%.
This paper presents a methodology to design a discrete-event system (DES) for the online supervision of a biotechnological process. The DES is synthesised applying wavelet transform and inductive logic programming on ...
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This paper presents a methodology to design a discrete-event system (DES) for the online supervision of a biotechnological process. The DES is synthesised applying wavelet transform and inductive logic programming on the measured signals constrained to the biotechnologist expert validation.
Support logic programming and its practical implementation (Fril) integrates probabilistic and fuzzy uncertainty into logic programming using mass assignments. This paper presents a snapshot of current research, aimed...
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Support logic programming and its practical implementation (Fril) integrates probabilistic and fuzzy uncertainty into logic programming using mass assignments. This paper presents a snapshot of current research, aimed at combining the best aspects of inductive logic programming with the uncertainty representation of Fril to create a sophisticated and novel approach to knowledge discovery. An example is given showing how a supported Fril rule can be extracted from uncertain Fril relations.
Datalog programs containing a unique rule and possibly some facts are known as single rule programs, or sirups. We study the complexity of evaluating sirups over variable and fixed databases, respectively, as well as ...
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Datalog programs containing a unique rule and possibly some facts are known as single rule programs, or sirups. We study the complexity of evaluating sirups over variable and fixed databases, respectively, as well as the descriptive complexity of sirups, i.e., their expressive power. In all cases it turns out that even very restricted classes of sirups have the same complexity and essentially the same expressive power as general datalog programs. In particular, the evaluation of single clause programs is EXPTIME complete (combined complexity) and, if restricted to linear recursive rules, PSPACE complete. Moreover, sirups with one recursive rule and one fact capture PTIME on ordered structures, if a certain data representation is assumed and certain predefined relations are provided. We also prove that the datalog clause implication problem, i.e., deciding whether a datalog clause implies another one, is EXPTIME complete. Our main technical tool is a product construction which maps a datalog programs to an essentially equivalent sirup. (C) 2003 Published by Elsevier Science (USA).
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