Case-Based Reasoning Research and Development: 8th by Lorraine McGinty, David C. Wilson

By Lorraine McGinty, David C. Wilson

This e-book 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 provided including 2 invited talks have been conscientiously reviewed and chosen from fifty five submissions. overlaying a variety of CBR issues of curiosity either to practitioners and researchers, the papers are dedicated to theoretical/methodological in addition to to applicative points of present CBR research.

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A reward of 0 is received for all actions before the end of the play. 2%), interceptions, and fumbles (combined: 1%-3%) rarely occur. For the target task, our learning agent attempts to control the QB’s actions so as to maximize total reward. The QB can perform one of eight actions (see Figure 2) at each time step. The first four (Forward, Back, Left, and Right) cause the QB to move in a certain direction for one time step. Three more cause the QB to pass to a receiver (who is running a pre-determined pass route): Throw RB, Throw WR1, and Throw WR2.

SIE2 is determined to be reusable because all cases (C2 , C4 & C5 ) activated by the query in the CRN are also activated by the SIE2 node. On the other hand, SIE4 is likely to need revision because it only activates one (C2 ) out of the three cases activated by the query in the CRN. 4 Evidence for Annotations: Neighbouring vs. All Cases We illustrated a simple view of our reuse architecture in figure 2 with six cases being considered in the creation of the sets RS1 and RS2 . However, a casebase will contain many more cases.

Springer, Heidelberg (2008) 16. Fellbaum, C. ): WordNet: An Electronic Lexical Database. edu Case-Based Reasoning in Transfer Learning David W. edu Abstract. Positive transfer learning (TL) occurs when, after gaining experience from learning how to solve a (source) task, the same learner can exploit this experience to improve performance and/or learning on a different (target) task. TL methods are typically complex, and case-based reasoning can support them in multiple ways. We introduce a method for recognizing intent in a source task, and then applying that knowledge to improve the performance of a case-based reinforcement learner in a target task.

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