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Probabilistic Modelling, Inference and Learning using Logical Theories
"... This paper provides a study of probabilistic modelling, inference and learning in a logicbased setting. We show how probability densities, being functions, can be represented and reasoned with naturally and directly in higherorder logic, an expressive formalism not unlike the (informal) everyday l ..."
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Cited by 9 (3 self)
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This paper provides a study of probabilistic modelling, inference and learning in a logicbased setting. We show how probability densities, being functions, can be represented and reasoned with naturally and directly in higherorder logic, an expressive formalism not unlike the (informal) everyday language of mathematics. We give efficient inference algorithms and illustrate the general approach with a diverse collection of applications. Some learning issues are also considered.
K.S.: Learning modal theories
 Proceedings of the 16th International Conference on Inductive Logic Programming (ILP 2006), Springer, LNAI 4455 (2007) 320–334
, 2007
"... Abstract. This paper discusses how to learn theories that are modal, concentrating on the issue of how modal hypotheses are formed. Illustrations are given to show the usefulness of the ideas for agent applications. 1 ..."
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Cited by 6 (6 self)
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Abstract. This paper discusses how to learn theories that are modal, concentrating on the issue of how modal hypotheses are formed. Illustrations are given to show the usefulness of the ideas for agent applications. 1
K.S.: Reflections on agent beliefs
 In: Proceedings of the Declarative Agent Languages and Technologies Workshop (DALT
, 2007
"... Abstract. Some issues concerning beliefs of agents are discussed. These issues are the general syntactic form of beliefs, the logic underlying beliefs, acquiring beliefs, and reasoning with beliefs. The logical setting is more expressive and aspects of the reasoning and acquisition processes are mor ..."
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Cited by 4 (4 self)
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Abstract. Some issues concerning beliefs of agents are discussed. These issues are the general syntactic form of beliefs, the logic underlying beliefs, acquiring beliefs, and reasoning with beliefs. The logical setting is more expressive and aspects of the reasoning and acquisition processes are more general than are usually considered. 1
Probabilistic and logical beliefs
 Proceedings of the Workshop on Languages, Methodologies and Development Tools for Multiagent Systems (LADS’007
, 2007
"... Abstract. This paper proposes a method of integrating two different concepts of belief in artificial intelligence: belief as a probability distribution and belief as a logical formula. The setting for the integration is a highly expressive logic. The integration is explained in detail, as its compar ..."
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Cited by 3 (3 self)
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Abstract. This paper proposes a method of integrating two different concepts of belief in artificial intelligence: belief as a probability distribution and belief as a logical formula. The setting for the integration is a highly expressive logic. The integration is explained in detail, as its comparison to other approaches to integrating logic and probability. An illustrative example is given to motivate the usefulness of the ideas in agent applications. 1
Declarative Programming for Agent Applications
"... This paper introduces the computational model of a declarative programming language intended for agent applications. Features supported by the language include functional and logic programming idioms, higherorder functions, modal computation, probabilistic computation, and some theoremproving capa ..."
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Cited by 1 (1 self)
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This paper introduces the computational model of a declarative programming language intended for agent applications. Features supported by the language include functional and logic programming idioms, higherorder functions, modal computation, probabilistic computation, and some theoremproving capabilities. The need for these features is motivated and examples are given to illustrate the central ideas.
Abstract Draft – Do not distribute Modal Functional Logic Programming
"... This paper introduces aspects of a novel modal functional logic programming language called Bach that is an extension of the existing functional logic language Escher. Language facilities available in Bach but not in Escher include (1) support for modalities and (2) an improved theoremproving capab ..."
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This paper introduces aspects of a novel modal functional logic programming language called Bach that is an extension of the existing functional logic language Escher. Language facilities available in Bach but not in Escher include (1) support for modalities and (2) an improved theoremproving capability. We show how the increased expressiveness of Bach can be exploited to produce easytounderstand programs for solving a variety of computational problems that arise in applications, especially agents applications.
Abstract Probabilistic Reasoning in a Classical Logic
"... We offer a view on how probability is related to logic. Specifically, we argue against the widely held belief that standard classical logics have no direct way of modelling the certainty of assumptions in theories and no direct way of stating the certainty of theorems proved from these (uncertain) a ..."
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We offer a view on how probability is related to logic. Specifically, we argue against the widely held belief that standard classical logics have no direct way of modelling the certainty of assumptions in theories and no direct way of stating the certainty of theorems proved from these (uncertain) assumptions. The argument rests on the observation that probability densities, being functions, can be represented and reasoned with naturally and directly in (classical) higherorder logic. Key words: probabilistic reasoning, classical logic, higherorder logic, integrating logic and probability 1.