Abductive explanation-based learning: A solution to the multiple inconsistent explanation problem

Cohen, William W.

doi:10.1007/bf00992863

Cited by 15 publications

(7 citation statements)

References 26 publications

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“…In this paper, three currently active research programs in machine learning (integrating multiple classifiers (Chan and Stolfo, 1995;Breiman, 1996aBreiman, , 1996bTing, 1996;Drucker, 1997;Ting and Witten, 1997), theory revision (Bergadano and Giordana, 1988;Flann and Dietterich, 1989;Towell et al, 1990;Ourston, 1991;Cohen, 1992;Baffes and Mooney, 1993;Michalski, 1993;Mooney, 1993;Schaffer, 1993;Koppel et al, 1994;Richards and Mooney, 1995) and bias selection (Merz, 1998;Merz, 1995;Ho et al, 1994;Brodley, 1993)) are viewed from a single perspective. The goal of integrating multiple classifiers is to improve the performance and scalability of learning algorithms by generating multiple classifiers, running them on distributed systems, and combining their results.…”

Section: Introductionmentioning

confidence: 99%

Arbitrating Among Competing Classifiers Using Learned Referees

Ortega¹,

Koppel

Argamon

2001

Knowledge and Information Systems

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The situation in which the results of several different classifiers and learning algorithms are obtainable for a single classification problem is common. In this paper, we propose a method that takes a collection of existing classifiers and learning algorithms, together with a set of available data, and creates a combined classifier that takes advantage of all of these sources of knowledge. The basic idea is that each classifier has a particular subdomain for which it is most reliable. Therefore, we induce a referee for each classifier, which describes its area of expertise. Given such a description, we arbitrate between the component classifiers by using the most reliable classifier for the examples in each subdomain. In experiments in several domains, we found such arbitration to be significantly more effective than various voting techniques which do not seek out subdomains of expertise. Our results further suggest that the more fine grained the analysis of the areas of expertise of the competing classifiers, the more effectively they can be combined. In particular, we find that classification accuracy increases greatly when using intermediate subconcepts from the classifiers themselves as features for the induction of referees.

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Section: Introductionmentioning

confidence: 99%

Arbitrating Among Competing Classifiers Using Learned Referees

Ortega¹,

Koppel

Argamon

2001

Knowledge and Information Systems

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“…Various methods were used to allow to learn the optimal opening bid: abductive explanation-based learning [48], artificial neural networks [49], [50], probabilistic neural networks with evolutionary programming-based clustering technique [51], or roughfuzzy set theory [52].…”

Section: A the Bidding Phasementioning

confidence: 99%

Learning Without Human Expertise: A Case Study of the Double Dummy Bridge Problem

Mossakowski

Mańdziuk

2009

IEEE Trans. Neural Netw.

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Artificial neural networks, trained only on sample deals, without presentation of any human knowledge or even rules of the game, are used to estimate the number of tricks to be taken by one pair of bridge players in the so-called double dummy bridge problem (DDBP). Four representations of a deal in the input layer were tested leading to significant differences in achieved results. In order to test networks' abilities to extract knowledge from sample deals, experiments with additional inputs representing estimators of hand's strength used by humans were also performed. The superior network trained solely on sample deals outperformed all other architectures, including those using explicit human knowledge of the game of bridge. Considering the suit contracts, this network, in a sample of 100,000 testing deals, output a perfect answer in 53.11% of the cases and only in 3.52% of them was mistaken by more than one trick. The respective figures for notrump contracts were equal to 37.80% and 16.36%. The above results were compared with the ones obtained by 24 professional human bridge players--members of The Polish Bridge Union--on test sets of sizes between 27 and 864 deals per player (depending on player's time availability). In case of suit contracts, the perfect answer was obtained in 53.06% of the testing deals for ten upper-classified players and in 48.66% of them, for the remaining 14 participants of the experiment. For the notrump contracts, the respective figures were equal to 73.68% and 60.78%. Except for checking the ability of neural networks in solving the DDBP, the other goal of this research was to analyze connection weights in trained networks in a quest for weights' patterns that are explainable by experienced human bridge players. Quite surprisingly, several such patterns were discovered (e.g., preference for groups of honors, drawing special attention to Aces, favoring cards from a trump suit, gradual importance of cards in one suit--from two to the Ace, etc.). Both the numerical figures and weight patterns are stable and repeatable in a sample of neural architectures (differing only by randomly chosen initial weights). In summary, the piece of research described in this paper provides a detailed comparison between various data representations of the DDBP solved by neural networks. On a more general note, this approach can be extended to a certain class of binary classification problems.

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“…One of the earliest uses of EBL techniques to guide concept induction in structured domains was through theory specialization [14,6]. These systems utilize background knowledge represented as an overly-genera] domain theory.…”

Section: Theory Specializationmentioning

confidence: 99%

Integrating ILP and EBL

Mooney¹,

Zelle²

1994

SIGART Bull.

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This paper presents a review of recent work that integrates methods from Inductive Logic Programming (ILP) and Explanation-Based Learning (EBL). ILP and EBL methods have complementary strengths and weaknesses and a number of recent projects have effectively combined them into systems with better performance than either of the individual approaches. In particular, integrated systems have been developed for guiding induction with prior knowledge (ML-Smart, FOCL, GREN-DEL) refining imperfect domain theories (FORTE, AUDREY, Rx), and learning effective search-control knowledge (AxA-EBL, DOLPHIN).

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Abductive explanation-based learning: A solution to the multiple inconsistent explanation problem

Cited by 15 publications

References 26 publications

Arbitrating Among Competing Classifiers Using Learned Referees

Arbitrating Among Competing Classifiers Using Learned Referees

Learning Without Human Expertise: A Case Study of the Double Dummy Bridge Problem

Integrating ILP and EBL

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