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1 Consider the problem of learning the target concept "pairs of people who live in 11 the same house," denoted by the predicate HouseMates(x, y) Below is a positive example of the concept
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HouseMates(Joe, Sue) Person( Joe) Sex(Joe, Male) Hair Color (Joe, Black) Person(Sue) Sex(Sue, Female) Haircolor (Sue, Brown)
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Height ( J o e , Short) Nationality(Joe, U S ) Mother(Joe, Mary) Age ( J o e , 8)
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The following domain theory is helpful for acquiring the HouseMates concept:
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HouseMates(x, y ) t InSameFamily(x, y ) HouseMates(x, y ) t FraternityBrothers(x, y ) InSameFamily(x, y ) t M a r r i e d ( x , y ) InSameFamily ( x , y ) t Youngster(x) A Youngster ( y ) A SameMother ( x , y ) SameMother(x, y ) t Mother(x, z ) A M o t h e r ( y , z ) Youngster(x) t Age(x, a ) A LessThan(a, 10)
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Apply the PROLOG-EBG algorithm to the task of generalizing from the above instance, using the above domain theory In particular, ( a ) Show a hand-trace of the PROLOG-EBG algorithm applied to this problem; that is, show the explanation generated for the training instance, show the result of regressing the target concept through this explanation, and show the resulting Horn clause rule (b) Suppose that the target concept is "people who live with Joe" instead of "pairs of people who live together" Write down this target concept in terms of the above formalism Assuming the same training instance and domain theory as before, what Horn clause rule will PROLOG-EBG produce for this new target concept can As noted in Section 1131, PROLOG-EBG construct useful new features that are not explicit features of the instances, but that are defined in terms of the explicit features and that are useful for describing the appropriate generalization These features are derived as a side effect of analyzing the training example explanation A second method for deriving useful features is the BACKPROPAGATION algorithm for multilayer neural networks, in which new features are learned by the hidden units based on the statistical properties of a large number of examples Can you suggest a way in which one might combine these analytical and inductive approaches to generating new features (Warning: This is an open research problem)
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Ahn, W, Mooney, R J, Brewer, W F, & DeJong, G F (1987) Schema acquisition from one example: Psychological evidence for explanation-based learning Ninth Annual Conference of the Cognitive Science Society (pp 50-57) Hillsdale, NJ: Lawrence Erlbaum Associates Bennett, S W, & DeJong, G F (1996) Real-world robotics: Learning to plan for robust execution Machine k a m i n g , 23, 121 Carbonell, J, Knoblock, C, & Minton, S (1990) PRODIGY: integrated architecture for planning An and learning In K VanLehn (Ed), Architectures for Intelligence Hillsdale, NJ: Lawrence Erlbaum Associates Chien, S (1993) NONMON: Learning with recoverable simplifications In G DeJong (Ed), Znvestigating explanation-based learning (pp 4 1 M 3 4 ) Boston, MA: Kluwer Academic Publishers f Davies, T R, and Russell, S J (1987) A logical approach to reasoning by analogy Proceedings o the 10th International Joint Conference on ArtiJcial Intelligence (pp 264-270) San Mateo, CA: Morgan Kaufmann
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DeJong, G (1981) Generalizations based on explanations Proceedings of the Seventh International Joint Conference on ArtiJicial Intelligence (pp 67-70) DeJong, G, & Mooney, R (1986) Explanation-based learning: An alternative view Machine Learning, 1(2), 145-176 DeJong, G (Ed) (1993) Investigating explanation-based learning Boston, MA: Kluwer Academic Publishers DeJong, G (1994) Learning to plan in continuous domains ArtiJicial Intelligence, 64(1), 71-141 DeJong, G (1997) Explanation-based learning In A Tucker (Ed), The Computer Science and Engineering Handbook (pp 499-520) Boca Raton, FL: CRC Press Dietterich, T G, Flann, N S (1995) Explanation-based learning and reinforcement learning: A unified view Proceedings of the 12th International Conference on Machine Learning (pp 176-184) San Mateo, CA: Morgan Kaufmann Doorenbos, R E (1993) Matching 100,000 learned rules Proceedings of the Eleventh National Conference on ArtiJicial Intelligence (pp 290-296) AAAI Press/MIT Press Fikes, R, Hart, P, & Nisson, N (1972) Learning and executing generalized robot plans ArtiJicial Intelligence, 3(4), 251-288 Fisher, D, Subrarnanian, D, & Tadepalli, P (1992) An overview of current research on knowledge compilation and speedup learning Proceedings of the Second International Workshop on Knowledge Compilation and Speedup Learning Flann, N S, & Dietterich, T G (1989) A study of explanation-based methods for inductive learning Machine Learning, 4, 187-226 Gervasio, M T, & DeJong, G F (1994) An incremental learning approach to completable planning Proceedings of the Eleventh International Conference on Machine Learning, New Brunswick, NJ San Mateo, CA: Morgan Kaufmann van Harmelen, F, & Bundy, A (1988) Explanation-based generalisation = partial evaluation Artificial Intelligence, 36(3), 401-412 Kedar-Cabelli, S, & McCarty, T (1987) Explanation-based generalization as resolution theorem proving Proceedings of the Fourth International Workshop on Machine Learning (pp 383389) San Francisco: Morgan Kaufmann Kotovsky, L, & Baillargeon, R (1994) Calibration-based reasoning about collision events in 11month-old infants Cognition, 51, 107-129 The Laird, J E, Rosenbloom, P S, & Newell, A (1986) Chunking in SOAR: anatomy of a general learning mechanism Machine Learning, 1, 11 Mahadevan, S, Mitchell, T, Mostow, D J, Steinberg, L, & Tadepalli, P (1993) An apprenticebased approach to knowledge acquisition In S Mahadevan, T Mitchell, D J Mostow, L Steinberg, & P Tadepalli (Eds), ArtiiJicial Intelligence, 64(1), 1-52 Minton, S (1988) Learning search control knowledge: An explanation-based approach Boston, MA: Kluwer Academic Publishers Miton, S, Carbonell, J, Knoblock, C, Kuokka, D, Etzioni, O, & Gil, Y (1989) Explanation-based leaming: A problem solving perspective ArtiJicial Intelligence, 40, 63-1 18 Minton, S (1990) Quantitative results concerning the utility of explanation-based leaming ArtiJicial Intelligence, 42, 363-391 Mitchell, T M (1981) Toward combining empirical and analytical methods for inferring heuristics (Technical Report LCSR-TR-27), Rutgers Computer Science Department (Also reprinted in A Elithorn & R Banerji (Eds), ArtiJicial and Human Intelligence North-Holland, 1984) Mitchell, T M (1983) Learning and problem-solving Proceedings of the Eighth International Joint Conference on ArtiiJicial Intelligence San Francisco: Morgan Kaufmann Mitchell, T M, Keller, R, & Kedar-Cabelli, S (1986) Explanation-based generalization: A unifying view Machine Learning, 1(1), 47-80 Mitchell, T M (1990) Becoming increasingly reactive Proceedings of the Eighth National Conference on ArtQicial Intelligence Medo Park, CA: AAAI Press Mitchell, T M, & Thrun, S B (1993) Explanation-based neural network learning for robot control In S Hanson et al (Eds), Advances in neural infomtionprocessing systems 5 (pp 287-2941 San Mateo, CA: Morgan-Kaufmann Press
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