By Lorraine McGinty, David C. Wilson
This publication constitutes the refereed complaints of the eighth overseas convention on Case-Based Reasoning, ICCBR 2009, held in Seattle, WA, united states, in July 2009. The 17 revised complete papers and 17 revised poster papers awarded including 2 invited talks have been rigorously reviewed and chosen from fifty five submissions. masking a variety of CBR issues of curiosity either to practitioners and researchers, the papers are dedicated to theoretical/methodological in addition to to applicative facets of present CBR research.
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G gave and given). Figure 7 shows an average accuracy graph comparing the baseline (CRN), CR2N and CG from our experiments with the H&S incident reports. The performance of the reuse techniques exceed the baseline as shown by their accuracy 26 I. Adeyanju et al. plots. There is no clear distinction between CR2N and CG’s performance but CR2N is marginally better with 6 wins out of the ten neighbourhood sizes evaluated. Overall, CR2N is most consistent with an initial increase in accuracy followed by a decrease that tappers as the neighbourhood size increases.
For example, Kuhlmann and Stone (2007) enumerate a constrained space of related game descriptions and store graph representations of them as cases. , related game variants). , 1989) to perform source-target task mapping. Their cases encode agent actions in KeepAway soccer games as qualitative dynamic Bayesian networks. The focus is again on value function reuse. , as decision trees) indexed by the environment’s features. The policies are to be reused in similar environments. Sharma et al. (2007) focus on learning and reusing Q values for state-action pairs, where the feature vector states are case indices representing situations in a real-time strategy game.
Reward estimate) of each case is updated using: ݒൌ ݒ ߙߚሾ ݎ Ȗ ୟא ܳƍ ሺ ݏƍ ሻ െ ܳ ሺݏሻሿ. Finally, the solution values of all cases updated earlier in the current trial are updated according to their λ-eligibility: ݒൌ ݒ ሺȜȖሻ௧ ߙߚሾ ݎ Ȗ ୟא ܳƍ ሺ ݏƍ ሻ െ ܳ ሺݏሻሿ, where t is the number of steps between the earlier use and the current update, and 0≤λ<1 is the trace decay parameter. 1) can significantly increase a learner’s ability in the target task. 2 for the transfer and non-transfer conditions.