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Normal Forms for Defeasible Logic
 In Proc. Joint International Conference and Symposium on Logic Programming
, 1998
"... Defeasible logic is an important logicprogramming based nonmonotonic reasoning formalism which has an efficient implementation. It makes use of facts, strict rules, defeasible rules, defeaters, and a superiority relation. Representation results are important because they can help the assimilation o ..."
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Cited by 21 (13 self)
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Defeasible logic is an important logicprogramming based nonmonotonic reasoning formalism which has an efficient implementation. It makes use of facts, strict rules, defeasible rules, defeaters, and a superiority relation. Representation results are important because they can help the assimilation of a concept by confining attention to its critical aspects. In this paper we derive some representation results for defeasible logic. In particular we show that the superiority relation does not add to the expressive power of the logic, and can be simulated by other ingredients in a modular way. Also, facts can be simulated by strict rules. Finally we show that we cannot simplify the logic any further in a modular way: Strict rules, defeasible rules, and defeaters form a minimal set of independent ingredients in the logic. 1 Introduction Normal forms play an important role in computer science. Examples of areas where normal forms have proved fruitful include logic [10], where normal forms o...
Semantics for using Stochastic Constraint Solvers in Constraint Logic Programming
 Journal of Functional and Logic Programming
, 1998
"... This paper proposes a number of models for integrating stochastic constraint solvers into constraint logic programming systems in order to solve constraint satisfaction problems efficiently. Stochastic solvers can solve hard constraint satisfaction problems very efficiently, and constraint logic ..."
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Cited by 5 (1 self)
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This paper proposes a number of models for integrating stochastic constraint solvers into constraint logic programming systems in order to solve constraint satisfaction problems efficiently. Stochastic solvers can solve hard constraint satisfaction problems very efficiently, and constraint logic programming allows heuristics and problem breakdown to be encoded in the same language as the constraints. Hence their combination is attractive. Unfortunately there is a mismatch in the kind of information a stochastic solver provides, and that which a constraint logic programming system requires. We study the semantic properties of the various models of constraint logic programming systems that make use of stochastic solvers, and give soundness and completeness results for their use. We describe an example system we have implemented using a modified neural network simulator, GENET, as a constraint solver. We briefly compare the efficiency of these models against the propagation base...
A Logic for Modeling the Dynamics of Beliefs in Cryptographic Protocols
 IN: PROCEEDINGS OF THE AUSTRALASIAN CONFERENCE ON COMPUTER SCIENCE
, 2001
"... We present a logic of modeling the dynamics of beliefs in cryptographic protocols. Differently from previous proposals, our logic is situation based in which a protocol is viewed as a finite sequence of actions performed by various principals at different situations, and each action is a primitive t ..."
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Cited by 4 (0 self)
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We present a logic of modeling the dynamics of beliefs in cryptographic protocols. Differently from previous proposals, our logic is situation based in which a protocol is viewed as a finite sequence of actions performed by various principals at different situations, and each action is a primitive term in the language. Therefore, it becomes possible to model the dynamic change of each principal's beliefs at each step of the protocol within the logic system. Our logic has a precise semantics and is sound with respect to the underlying axiomatic system.
Effective Metaprogramming in Declarative Languages
, 1998
"... Declarative metaprogramming is vital, since it is the most promising means by which programs can be made to reason about other programs. A metaprogram is a program that takes another program, called the object program, as data. A declarative programming language is a programming language based on a ..."
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Cited by 2 (0 self)
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Declarative metaprogramming is vital, since it is the most promising means by which programs can be made to reason about other programs. A metaprogram is a program that takes another program, called the object program, as data. A declarative programming language is a programming language based on a logic that has a model theory. A metaprogram operates on a representation of an object...
A Formal Logical Analysis of Causal Relations Summary
"... Causal relations of various kinds are a pervasive feature of human language and theorising about the world. Despite this, the specification of a satisfactory general analysis of causal relations has long proved difficult. The research described in this thesis is an attempt to provide a formal logica ..."
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Causal relations of various kinds are a pervasive feature of human language and theorising about the world. Despite this, the specification of a satisfactory general analysis of causal relations has long proved difficult. The research described in this thesis is an attempt to provide a formal logical theory of causal relations, in a broad sense of ‘causal’, which includes various atemporal explanatory and functional relations, in addition to causation between temporally ordered events; and which involves not only necessity associated with physical laws, but also necessity associated with laws and constraints of various other types. The key idea which motivates the analysis is that many types of causal relation have in common certain underlying abstract properties, regardless of the nature of the participants involved. These properties can be expressed via an axiomatisation, initially viewed as applicable to ‘event causation’, but subsequently reinterpreted in a more abstract and general way. Given the wide variety of models for the axioms, there are not likely to be powerful general methods for computing the causal relationships defined: instead it is likely to