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A case for abductive reasoning over ontologies.
- In Proceedings of OWL: Experiences and Directions.
, 2006
"... ..."
Logical Characterisations Of Inductive Learning
, 2000
"... This chapter presents a logical analysis of induction. Contrary to common approaches to inductive logic that treat inductive validity as a real-valued generalisation of deductive validity, we argue that the only logical step in induction lies in hypothesis generation rather than evaluation. ..."
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Cited by 13 (2 self)
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This chapter presents a logical analysis of induction. Contrary to common approaches to inductive logic that treat inductive validity as a real-valued generalisation of deductive validity, we argue that the only logical step in induction lies in hypothesis generation rather than evaluation.
Inverse Entailment for Full Clausal Theories
- In: LICS-2001 Workshop on Logic and Learning
, 2001
"... This paper shows a sound and complete method for inverse entailment in inductive logic programming. We show that inverse entailment can be computed with a resolution method for consequencefinding. In comparison with previous work, induction via consequence-finding is sound and complete for finding h ..."
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Cited by 6 (0 self)
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This paper shows a sound and complete method for inverse entailment in inductive logic programming. We show that inverse entailment can be computed with a resolution method for consequencefinding. In comparison with previous work, induction via consequence-finding is sound and complete for finding hypotheses from full clausal theories, and can be used for inducing not only definite clauses but also non-Horn clauses and integrity constraints.
Collaborative Inductive Logic Programming for Path Planning
"... In distributed systems, learning does not necessarily involve the participation of agents directly in the inductive process itself. Instead, many systems frequently employ multiple instances of induction separately. In this paper, we develop and evaluate a new approach for learning in distributed sy ..."
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Cited by 4 (0 self)
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In distributed systems, learning does not necessarily involve the participation of agents directly in the inductive process itself. Instead, many systems frequently employ multiple instances of induction separately. In this paper, we develop and evaluate a new approach for learning in distributed systems that tightly integrates processes of induction between agents, based on inductive logic programming techniques. The paper’s main contribution is the integration of an epistemic approach to reasoning about knowledge with inverse entailment during induction. The new approach facilitates a systematic approach to the sharing of knowledge and invention of predicates only when required. We illustrate the approach using the well-known path planning problem and compare results empirically to (multiple instances of) single agent-based induction over varying distributions of data. Given a chosen path planning algorithm, our algorithm enables agents to combine their local knowledge in an effective way to avoid central control while significantly reducing communication costs. 1
Brave Induction Revisited
"... Abstract. Sakama and Inoue introduced brave induction as a novel logic framework for concept-learning. They showed that brave induction has potential applications for problem solving in many domains. In this paper, motivated from Shapiro's definition of model inference problems, we provide an ..."
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Abstract. Sakama and Inoue introduced brave induction as a novel logic framework for concept-learning. They showed that brave induction has potential applications for problem solving in many domains. In this paper, motivated from Shapiro's definition of model inference problems, we provide an optimization of brave induction called proper brave induction, which prefers hypotheses resulting fewer minimal models. We first propose formal definitions of proper brave induction for clausal theories and nonmonotonic logic programs, then investigate corresponding properties and develop an optimization procedure. At last, we analyze computational complexity of decision problems for proper brave induction in propositional case.
Dual Aspects of Abduction and Induction
"... Abstract. A new characterization of abduction and induction is proposed, which is based on the idea that the various aspects of the two kinds of inference rest on the essential features of increment of (so to speak, extensionalized) comprehension and, respectively, of extension of the terms involved ..."
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Abstract. A new characterization of abduction and induction is proposed, which is based on the idea that the various aspects of the two kinds of inference rest on the essential features of increment of (so to speak, extensionalized) comprehension and, respectively, of extension of the terms involved. These two essential features are in a reciprocal relation of duality, whence the highlighting of the dual aspects of abduction and induction. Remarkably, the increment of comprehension and of extension are dual ways to realize, in the limit, a `deductivization ' of abduction and induction in a similar way as the Closed World Assumption does in the case of the latter.
Toward Inductive Logic Programming for Collaborative Problem Solving
"... Abstract — In this paper, we tackle learning in distributed systems and the fact that learning does not necessarily involve the participation of agents directly in the inductive process itself. Instead, many systems frequently employ multiple instances of induction separately. The paper’s main contr ..."
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Abstract — In this paper, we tackle learning in distributed systems and the fact that learning does not necessarily involve the participation of agents directly in the inductive process itself. Instead, many systems frequently employ multiple instances of induction separately. The paper’s main contribution is a new approach that tightly integrates processes of induction between distributed agents, based on inductive logic programming techniques, for a wider class of problem solving tasks. The approach combines inverse entailment with an epistemic approach to reasoning about knowledge, facilitating a systematic approach to the sharing of knowledge and invention of predicates only when required. We illustrate the approach for learning declarative program fragments and for a well-known path planning problem and compare results empirically to (multiple instances of) single agent-based induction over varying distributions of data. Given a chosen path planning algorithm, our algorithm enables agents to combine their local knowledge in an effective way to avoid central control while significantly reducing communication costs. I.
