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Multidimensional Dynamic Knowledge Representation
, 2001
"... According to Dynamic Logic Programming (DLP), knowledge may be given by a set of theories (encoded as logic programs) representing different states of knowledge. These may represent time (in updates), specificity (in taxonomies), strength of updating instance (in the legislative domain), hierarchica ..."
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Cited by 26 (11 self)
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According to Dynamic Logic Programming (DLP), knowledge may be given by a set of theories (encoded as logic programs) representing different states of knowledge. These may represent time (in updates), specificity (in taxonomies), strength of updating instance (in the legislative domain), hierarchical position of knowledge source (in organizations), etc. The mutual relationships extant among states are used to determine the semantics of the combined theory composed of all the individual theories. Although suitable to encode a single dimension (e.g. time, hierarchies...), DLP cannot deal with more than one simultaneously because it is defined only for a linear sequence of states. To overcome this limitation, we introduce the notion of Multidimensional Dynamic Logic Programming (MDLP), which generalizes DLP to collections of states organized in arbitrary acyclic digraphs representing precedence. In this setting, MDLP assigns semantics to sets and subsets of such logic ...
Nonmonotonic Abductive Inductive Learning
 Journal of Applied Logic
, 2008
"... Inductive Logic Programming (ILP) is concerned with the task of generalising sets of positive and negative examples with respect to background knowledge expressed as logic programs. Negation as Failure (NAF) is a key feature of logic programming which provides a means for nonmonotonic commonsense re ..."
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Cited by 24 (6 self)
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Inductive Logic Programming (ILP) is concerned with the task of generalising sets of positive and negative examples with respect to background knowledge expressed as logic programs. Negation as Failure (NAF) is a key feature of logic programming which provides a means for nonmonotonic commonsense reasoning under incomplete information. But, so far, most ILP research has been aimed at Horn programs which exclude NAF, and has failed to exploit the full potential of normal programs that allow NAF. By contrast, Abductive Logic Programming (ALP), a related task concerned with explaining observations with respect to a prior theory, has been well studied and applied in the context of normal logic programs. This paper shows how ALP can be used to provide a semantics and proof procedure for nonmonotonic ILP that utilises practical methods of language and search bias to reduce the search space. This is done by lifting an existing method called Hybrid Abductive Inductive Learning (HAIL) from Horn clauses to normal logic programs. To demonstrate its potential benefits, the resulting system, called XHAIL, is applied to a process modelling case study involving a nonmonotonic temporal Event Calculus (EC). 1
Induction from answer sets in nonmonotonic logic programs
 ACM Transactions on Computational Logic
"... Inductive logic programming (ILP) realizes inductive machine learning in computational logic. However, the present ILP mostly handles classical clausal programs, especially Horn logic programs, and has limited applications to learning nonmonotonic logic programs. This article studies a method for re ..."
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Cited by 12 (0 self)
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Inductive logic programming (ILP) realizes inductive machine learning in computational logic. However, the present ILP mostly handles classical clausal programs, especially Horn logic programs, and has limited applications to learning nonmonotonic logic programs. This article studies a method for realizing induction in nonmonotonic logic programs. We consider an extended logic program as a background theory, and introduce techniques for inducing new rules using answer sets of the program. The produced new rules explain positive/negative examples in the context of inductive logic programming. The proposed methods extend the present ILP techniques to a syntactically and semantically richer framework, and contribute to a theory of nonmonotonic ILP.
On the use of multidimensional dynamic logic programming to represent societal agents’ viewpoints
 IN PROCS OF EPIA '01, VOLUME 2258 OF LNAI
, 2001
"... This paper explores the applicability of the new paradigm of Multidimensional Dynamic Logic Programming to represent an agent’s view of the combination of societal knowledge dynamics. The representation of a dynamic society of agents is the core of MIN ERVA [11], an agent architecture and system ..."
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Cited by 10 (8 self)
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This paper explores the applicability of the new paradigm of Multidimensional Dynamic Logic Programming to represent an agent’s view of the combination of societal knowledge dynamics. The representation of a dynamic society of agents is the core of MIN ERVA [11], an agent architecture and system designed with the intention of providing a common agent framework based on the unique strengths of Logic Programming, hat allows the combination of several nonmonotonic knowledge representation and reasoning mechanisms developed in recent years.
Learning to Reason about Actions
 In W. Horn (Ed.), Proceedings of the 14th European Conference on Arti Intelligence
"... We focus on learning representations of dynamical systems that can be characterized by logicbased formalisms for reasoning about actions and change, where system's behaviors are naturally viewed as appropriate logical consequences of the domain's description. To this end, logicbased indu ..."
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Cited by 9 (4 self)
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We focus on learning representations of dynamical systems that can be characterized by logicbased formalisms for reasoning about actions and change, where system's behaviors are naturally viewed as appropriate logical consequences of the domain's description. To this end, logicbased induction methods are adapted to identify the input/output behavior of a dynamical system corresponding to an environment. The study of dynamic domains is started with domains modelable with classical action theories and is progressively enhanced to manage more complex behaviors.
Learning in Clausal Logic: A Perspective on Inductive Logic Programming
 Computational Logic: Logic Programming and Beyond, volume 2407 of Lecture Notes in Computer Science
, 2002
"... Abstract. Inductive logic programming is a form of machine learning from examples which employs the representation formalism of clausal logic. One of the earliest inductive logic programming systems was Ehud Shapiro’s Model Inference System [90], which could synthesise simple recursive programs like ..."
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Cited by 5 (0 self)
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Abstract. Inductive logic programming is a form of machine learning from examples which employs the representation formalism of clausal logic. One of the earliest inductive logic programming systems was Ehud Shapiro’s Model Inference System [90], which could synthesise simple recursive programs like append/3. Many of the techniques devised by Shapiro, such as topdown search of program clauses by refinement operators, the use of intensional background knowledge, and the capability of inducing recursive clauses, are still in use today. On the other hand, significant advances have been made regarding dealing with noisy data, efficient heuristic and stochastic search methods, the use of logical representations going beyond definite clauses, and restricting the search space by means of declarative bias. The latter is a general term denoting any form of restrictions on the syntactic form of possible hypotheses. These include the use of types, input/output mode declarations, and clause schemata. Recently, some researchers have started using alternatives to Prolog featuring strong typing and real functions, which alleviate the need for some of the above adhoc mechanisms. Others have gone beyond Prolog by investigating learning tasks in which the hypotheses are not definite clause programs, but for instance sets of indefinite clauses or denials, constraint logic programs, or clauses representing association rules. The chapter gives an accessible introduction to the above topics. In addition, it outlines the main current research directions which have been strongly influenced by recent developments in data mining and challenging reallife applications. 1
Combining Societal Agents' Knowledge
 INFORMATICA, FACULDADE DE CIENCIAS E TECNOLOGIA, UNIVERSIDADE NOVA DE
, 2001
"... This paper explores the applicability of the new paradigm of Multidimensional Dynamic Logic Programming to represent an agent's view of the combination of societal knowledge dynamics. The representation of a dynamic society of agents is the core of MINERVA [11], an agent architecture and syste ..."
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Cited by 4 (2 self)
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This paper explores the applicability of the new paradigm of Multidimensional Dynamic Logic Programming to represent an agent's view of the combination of societal knowledge dynamics. The representation of a dynamic society of agents is the core of MINERVA [11], an agent architecture and system designed with the intention of providing a common agent framework based on the unique strengths of Logic Programming, hat allows the combination of several nonmonotonic knowledge representation and reasoning mechanisms developed in recent years.
Learning ThreeValued Logic Programs
, 1999
"... We show that the adoption of a threevalued setting for inductive concept learning is particularly useful for learning. Distinguishing between what is true, what is false and what is unknown can be useful in situations where decisions have to be taken on the basis of scarce information. In order to ..."
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Cited by 3 (0 self)
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We show that the adoption of a threevalued setting for inductive concept learning is particularly useful for learning. Distinguishing between what is true, what is false and what is unknown can be useful in situations where decisions have to be taken on the basis of scarce information. In order to learn in a threevalued setting, we adopt Extended Logic Programs (ELP) under a WellFounded Semantics with explicit negation (WFSX ) as the representation formalism for learning. Standard Inductive Logic Programming techniques are then employed to learn the concept and its opposite. The learnt definitions of the positive and negative concepts may overlap. In the paper, we handle the issue of combination of possibly contradictory learnt definitions, and we show strategies for theory refinement.
Enabling Agents to Update their Knowledge and to Prefer
"... Introduction In a previous paper [5] we presented a combination of the dynamic logic programming paradigm proposed by J. J. Alferes et al. [1, 10] and a version of KSagents proposed by Kowalski and Sadri [7]. In the resulting framework, rational, reactive agents can dynamically change their own kn ..."
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Cited by 3 (2 self)
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Introduction In a previous paper [5] we presented a combination of the dynamic logic programming paradigm proposed by J. J. Alferes et al. [1, 10] and a version of KSagents proposed by Kowalski and Sadri [7]. In the resulting framework, rational, reactive agents can dynamically change their own knowledge bases as well as their own goals. In particular, at every iteration of an observethinkact cycle, the agent can make observations, learn new facts and new rules from the environment, and then it can update its knowledge accordingly. The agent can also receive a piece of information that contrasts with its knowledge. To solve eventual cases of contradiction within the theory of an agent, techniques of contradiction removal and preferences among several sources can be adopted [8]. The actions of an agent are modeled by means of updates, inspired by the approach in [3]. A semantic characterization of updates is given in [1] as a generalization of the stable model semantics of n