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Learning Stochastic Logic Programs
, 2000
"... Stochastic Logic Programs (SLPs) have been shown to be a generalisation of Hidden Markov Models (HMMs), stochastic contextfree grammars, and directed Bayes' nets. A stochastic logic program consists of a set of labelled clauses p:C where p is in the interval [0,1] and C is a firstorder range ..."
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Cited by 1057 (71 self)
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Stochastic Logic Programs (SLPs) have been shown to be a generalisation of Hidden Markov Models (HMMs), stochastic contextfree grammars, and directed Bayes' nets. A stochastic logic program consists of a set of labelled clauses p:C where p is in the interval [0,1] and C is a firstorder rangerestricted definite clause. This paper summarises the syntax, distributional semantics and proof techniques for SLPs and then discusses how a standard Inductive Logic Programming (ILP) system, Progol, has been modied to support learning of SLPs. The resulting system 1) nds an SLP with uniform probability labels on each definition and nearmaximal Bayes posterior probability and then 2) alters the probability labels to further increase the posterior probability. Stage 1) is implemented within CProgol4.5, which differs from previous versions of Progol by allowing userdefined evaluation functions written in Prolog. It is shown that maximising the Bayesian posterior function involves nding SLPs with short derivations of the examples. Search pruning with the Bayesian evaluation function is carried out in the same way as in previous versions of CProgol. The system is demonstrated with worked examples involving the learning of probability distributions over sequences as well as the learning of simple forms of uncertain knowledge.
Learning logical definitions from relations
 MACHINE LEARNING
, 1990
"... Abstract. This paper describes FOIL, a system that learns Horn clauses from data expressed as relations. FOIL is based on ideas that have proved effective in attributevalue learning systems, but extends them to a firstorder formalism. This new system has been applied successfully to several tasks ..."
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Cited by 856 (8 self)
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Abstract. This paper describes FOIL, a system that learns Horn clauses from data expressed as relations. FOIL is based on ideas that have proved effective in attributevalue learning systems, but extends them to a firstorder formalism. This new system has been applied successfully to several tasks taken from the machine learning literature.
Knowledge Discovery in Databases: an Overview
, 1992
"... this article. 07384602/92/$4.00 1992 AAAI 58 AI MAGAZINE for the 1990s (Silberschatz, Stonebraker, and Ullman 1990) ..."
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Cited by 353 (3 self)
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this article. 07384602/92/$4.00 1992 AAAI 58 AI MAGAZINE for the 1990s (Silberschatz, Stonebraker, and Ullman 1990)
ExplanationBased Learning: An Alternative View
 Machine Learning
, 1986
"... Key words: machine learning, concept acquisition, explanationbased learning Abstract. In the last issue of this journal Mitchell, Keller, and KedarCabelli presented a unifying framework for the explanationbased approach to machine learning. While it works well for a number of systems, the framewo ..."
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Cited by 348 (19 self)
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Key words: machine learning, concept acquisition, explanationbased learning Abstract. In the last issue of this journal Mitchell, Keller, and KedarCabelli presented a unifying framework for the explanationbased approach to machine learning. While it works well for a number of systems, the framework does not adequately capture certain aspects of the systems under development by the explanationbased learning group at Illinois. The primary inadequacies arise in the treatment of concept operationality, organization of knowledge into schemata, and learning from observation. This paper outlines six specific problems with the previously proposed framework and presents an alternative generalization method to perform explanationbased learning of new concepts.
The Use of Explicit Plans to Guide Inductive Proofs
 9th Conference on Automated Deduction
, 1988
"... We propose the use of explicit proof plans to guide the search for a proof in automatic theorem proving. By representing proof plans as the specifications of LCFlike tactics, [Gordon et al 79], and by recording these specifications in a sorted metalogic, we are able to reason about the conjectures ..."
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Cited by 267 (38 self)
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We propose the use of explicit proof plans to guide the search for a proof in automatic theorem proving. By representing proof plans as the specifications of LCFlike tactics, [Gordon et al 79], and by recording these specifications in a sorted metalogic, we are able to reason about the conjectures to be proved and the methods available to prove them. In this way we can build proof plans of wide generality, formally account for and predict their successes and failures, apply them flexibly, recover from their failures, and learn them from example proofs. We illustrate this technique by building a proof plan based on a simple subset of the implicit proof plan embedded in the BoyerMoore theorem prover, [Boyer & Moore 79]. Keywords Proof plans, inductive proofs, theorem proving, automatic programming, formal methods, planning. Acknowledgements I am grateful for many long conversations with other members of the mathematical reasoning group, from which many of the ideas in this paper e...
Teleoreactive programs for agent control
 Journal of Artificial Intelligence Research
, 1994
"... A formalism is presented for computing and organizing actions for autonomous agents in dynamic environments. We introduce the notion of teleoreactive (TR) programs whose execution entails the construction of circuitry for the continuous computation of the parameters and conditions on which agent a ..."
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Cited by 196 (1 self)
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A formalism is presented for computing and organizing actions for autonomous agents in dynamic environments. We introduce the notion of teleoreactive (TR) programs whose execution entails the construction of circuitry for the continuous computation of the parameters and conditions on which agent action is based. In addition to continuous feedback, TR programs support parameter binding and recursion. A primary di erence between TR programs and many other circuitbased systems is that the circuitry of TR programs is more compact; it is constructed at run time and thus does not have toanticipate all the contingencies that might arise over all possible runs. In addition, TR programs are intuitive and easy to write and are written in a form that is compatible with automatic planning and learning methods. We brie y describe some experimental applications of TR programs in the control of simulated and actual mobile robots. 1.
Learning in the Presence of Concept Drift and Hidden Contexts
 Machine Learning
, 1996
"... . Online learning in domains where the target concept depends on some hidden context poses serious problems. A changing context can induce changes in the target concepts, producing what is known as concept drift. We describe a family of learning algorithms that flexibly react to concept drift and c ..."
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Cited by 190 (0 self)
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. Online learning in domains where the target concept depends on some hidden context poses serious problems. A changing context can induce changes in the target concepts, producing what is known as concept drift. We describe a family of learning algorithms that flexibly react to concept drift and can take advantage of situations where contexts reappear. The general approach underlying all these algorithms consists of (1) keeping only a window of currently trusted examples and hypotheses; (2) storing concept descriptions and reusing them when a previous context reappears; and (3) controlling both of these functions by a heuristic that constantly monitors the system's behavior. The paper reports on experiments that test the systems' performance under various conditions such as different levels of noise and different extent and rate of concept drift. Keywords: Incremental concept learning, online learning, context dependence, concept drift, forgetting 1. Introduction The work presen...
Refinement of Approximate Domain Theories by KnowledgeBased Neural Networks
 In Proceedings of the Eighth National Conference on Artificial Intelligence
, 1990
"... Standard algorithms for explanationbased learning require complete and correct knowledge bases. The KBANN system relaxes this constraint through the use of empirical learning methods to refine approximately correct knowledge. This knowledge is used to determine the structure of an artificial neural ..."
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Cited by 180 (15 self)
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Standard algorithms for explanationbased learning require complete and correct knowledge bases. The KBANN system relaxes this constraint through the use of empirical learning methods to refine approximately correct knowledge. This knowledge is used to determine the structure of an artificial neural network and the weights on its links, thereby making the knowledge accessible for modification by neural learning. KBANN is evaluated by empirical tests in the domain of molecular biology. Networks created by KBANN are shown to be superior, in terms of their ability to correctly classify unseen examples, to randomly initialized neural networks, decision trees, "nearest neighbor" matching, and standard techniques reported in the biological literature. In addition, KBANN's networks improve the initial knowledge in biologically interesting ways. Introduction Explanationbased learning (EBL) (Mitchell et al. 1986; DeJong & Mooney 1986) provides a way of incorporating preexisting knowledge i...
ELMART: An Adaptive Versatile System for WebBased Instruction
 Intâ€™l J. Artificial Intelligence in Education
, 2001
"... Abstract: This paper discusses the problems of developing versatile adaptive and intelligent learning systems that can be used in the context of practical Webbased education. We argue that versatility is an important feature of successful Webbased education systems. We introduce ELMART, an intell ..."
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Cited by 150 (13 self)
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Abstract: This paper discusses the problems of developing versatile adaptive and intelligent learning systems that can be used in the context of practical Webbased education. We argue that versatility is an important feature of successful Webbased education systems. We introduce ELMART, an intelligent interactive educational system to support learning programming in LISP. ELMART provides all learning material online in the form of an adaptive interactive textbook. Using a combination of an overlay model and an episodic student model, ELMART provides adaptive navigation support, course sequencing, individualized diagnosis of student solutions, and examplebased problemsolving support. Results of an empirical study show different effects of these techniques on different types of users during the first lessons of the programming course. ELMART demonstrates how some interactive and adaptive educational components can be implemented in WWW context and how multiple components can be naturally integrated together in a single system.
prodigy/analogy: Analogical Reasoning in General Problem Solving
, 1994
"... This paper describes the integration of analogical reasoning into general problem solving as a method of learning at the strategy level to solve problems more effectively. The method based on derivational analogy has been fully implemented in prodigy/analogy and proven empirically to be amenable t ..."
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Cited by 147 (18 self)
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This paper describes the integration of analogical reasoning into general problem solving as a method of learning at the strategy level to solve problems more effectively. The method based on derivational analogy has been fully implemented in prodigy/analogy and proven empirically to be amenable to scaling up both in terms of domain and problem complexity. prodigy/analogy addresses a set of challenging problems, namely: how to accumulate episodic problem solving experience, cases, how to define and decide when two problem solving situations are similar, how to organize a large library of planning cases so that it may be efficiently retrieved, and finally how to successfully transfer chains of problem solving decisions from past experience to new problem solving situations when only a partial match exists among corresponding problems. The paper discusses the generation and replay of the problem solving cases and we illustrate the algorithms with examples. We present briefly the librar...