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Table 3. Results on the at-tire domain, and the easy and hard 8-puzzle problems. Blackbox was run in its default mode, with -solver graphplan (BlackboxGP), and with -solver walksat (BlackboxWS). The planners implemented apply to problems for which the objective is to maximize the probability of satsifying the problem goals within the given time window, or the related goal of minimizing expected completion time. More gen- eral MDP problems are often given by specifying rewards for performing certain (noop or non-noop) actions. It appears that some of the kinds of information propagated here should be useful in those more general settings, and it would be interesting to see if this generalization could be made without a sacri ce in performance. Acknowledgements.This research is sponsored in part by NSF National Young Investigator grant CCR-9357793, NSF grant CCR-9732705 and an AT amp;T / Lu- cent Special Purpose Grant in Science and Technology. We would like to thank the anonymous reviewers for their detailed, thoughtful, and helpful comments.
1999
"... In PAGE 11: ... We consider the goal of achieving board state ABCDEFGH (reading left to right, top to bottom) from two di erent initial states: one in which a solution requires 18 steps and one in which a solution requires 30 steps (this is the case of initial board HGFEDCBA ). Results are given in Table3 . Note that PGraphplan is the fastest of all planners tested (even the deterministic ones) on this problem.... ..."
Cited by 59
Table 1 shows a simple example of performance matrix. The table shows the performance of a number of different learning materials in regard to a set of criteria thought to be relevant in a student agent choice between different options. These criteria are peer review average, member comments average, the number of assignments, personal collections where learning material can be found, availability statistics. In a basic form of MCA this performance matrix may be the final product of the analysis. The decision makers are then left with the task of assessing the extent to which their objectives are met by the entries in the matrix. In analytically more sophisticated MCA techniques the information in the basic matrix is usually converted into consistent numerical values.
Table 2 shows a better response to world 2. Subject comments indicated that reference-points nearby are very important for generating a feeling of height, such as being able to see the other levels of the diving tower through the floor-grid. The bridge over the swimming pool was considered to be very disturbing because this was not according to what subjects considered a real swimming pool. The volume of the sound of people talking was the same at every height and this also was remarked as being unrealistic. The thought that they could fall of the diving-board was very scary for most subjects.
2000
"... In PAGE 3: ... Table2 : Scores on fear and presence in pool . Scale is 1-7 Table 3 shows that world 3 was considered to be very scary only by one person and was not further considered.... ..."
Cited by 1
Table 1: THOUGHT EXPERIMENT METHOD
1992
Table 1: Example Angler thought Contributions
2005
"... In PAGE 3: ...g., from Table1 , thought 8 nearly subsumes thought 2). Sometimes two thoughts share a significant amount of semantic content, and although one is not sub- sumed by the other, the two are good candidates for merg- ing (e.... In PAGE 3: ...g., from Table1 thoughts 2, 4, and 8 or thoughts 19 and 21). Establishing semantic similarity among thought contribu- tions has two benefits.... ..."
Cited by 1
Table 3: Selected Similar Thought Matches
2005
"... In PAGE 5: ...thoughts. Table3 presents the best one or two matches for 14 of the thoughts using the four diferent types representation terms for the similarity asesment. In Table 3, the left-most column (A) presents the IDs of the thoughts for which the best matches are computed.... In PAGE 5: ... Table 3 presents the best one or two matches for 14 of the thoughts using the four diferent types representation terms for the similarity asesment. In Table3 , the left-most column (A) presents the IDs of the thoughts for which the best matches are computed. The remaining four columns each present the best matching thought and the score computed using one of the four types of representation data: tokens, lexemes, concepts, and ancestors.... In PAGE 5: ... One of the most desirable candidate matches is matching thoughts 2 and 8. Note in Table3 that thoughts 2 and 8 are high-ranked matches using the concept data and the ancestor data, but not using the lexeme nor the token data. The later types of data suport 2 and 18 as mutual best matches.... ..."
Cited by 1
Table 6: Interactions between fishers of different sectors. Absence of a remark indicates either no strong opinion expressed or no comment. Gear Type Fishing Area Person Other Sector
"... In PAGE 60: ... All static gear fishers who mentioned this issue thought the towed sector would be more likely to respect the IPA if anchored nets were not used inside the limits of the system. Two towed gear fishers also commented that some static gear fishers positioned gear outside the limits of the IPA ( Table6 , Appendix 1). One static gear fisher confirmed that some fishers did place traps outside the IPA area, and a number of trap strings from one fisher were consistently found located outside the IPA during the period of the study.... ..."
Table 1: Relation between Thought and Duration
"... In PAGE 3: ...024, one-tailed t-test, assuming normal distributions). Second, the author labeled all non-lexical utterances on a five-point scale, as seen in Table1 . Tokens involving more thought were generally longer, with the differences between t0 and t1, t2 and t3, and t3 and t4 significant by t-test.... ..."
Table 8 The relation between pattern on number thought experiment and matter and space thought experiments
2005
"... In PAGE 37: ... Nonetheless, an understanding of the continuity of matter itself (including its capacity to occupy space) appears to precede the understanding of number as inWnitely divisible. As seen in Table8 , about one-Wfth of the sample understood the former but not the latter, whereas the reverse was never true. Children understood weight and number as repeatedly divisible quantities at about the same time (see Table 9).... ..."
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