## Proceedings of the ... National Conference on Artificial Intelligence, Volume 14 |

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Page 510

1 (

MaxIncSize) Train = RANDOMSAMPLE(

Train repeat Theory =DOS(Train) NewTrain = 0 OldTest = 0 for

Test ...

1 (

**Examples**, InitSize, MaxIncSize) procedure Ww-DOS-95(**Examples**,InitSize,MaxIncSize) Train = RANDOMSAMPLE(

**Examples**,InitSize) Test =**Examples**\Train repeat Theory =DOS(Train) NewTrain = 0 OldTest = 0 for

**Example**€ TestTest ...

Page 524

The learning algorithm first finds correlations among pairs of rote-classifiers that

have the same meaning. The correlation between two rote-classifiers is

determined by the difference in their bit patterns. For

classifiers ...

The learning algorithm first finds correlations among pairs of rote-classifiers that

have the same meaning. The correlation between two rote-classifiers is

determined by the difference in their bit patterns. For

**example**, the pair of rote-classifiers ...

Page 592

In this paper, we are specifically considering the type of active learning in which

there exists a set of

for learning. Typically, the cycle proceeds as follows. The teacher presents the ...

In this paper, we are specifically considering the type of active learning in which

there exists a set of

**examples**, and the learner chooses which of these it will usefor learning. Typically, the cycle proceeds as follows. The teacher presents the ...

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### Contents

Agent Architecture | 3 |

Agent Coordination | 16 |

Negotiation | 29 |

Copyright | |

48 other sections not shown

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### Common terms and phrases

3SAT Abstract action agents algorithm allocation applied approach arc-consistency Artificial Intelligence assignment axioms Bayesian network behavior causal CBASlack chatter clauses coloring complexity component constraint satisfaction constraint satisfaction problems context dataset decision defined denoted described description logic distribution document domain dynamic emotion evaluation example expected value Figure formula function goal graph graph coloring GSAT heuristic commitments inference input literals local search logic Logic Programming method minimal node operators optimal paper parameters performance phase transition planning possible post-failure prob probability problem instances Proc procedure propagation qualitative quasigroup query plan random reasoning representation Research resource retrievable query robot robustness rule rule-base satisfied scheduling Selman semantics sequence servers simulation solution solve source-complete spatial specific strategies structure subgraph suffix tree SumHeight techniques temporal Theorem theory tion tree University unsatisfiable variables WSAT www.aaai.org