Abstract. In many Multi Agent Systems, under-education agents investigate their environments to discover their target(s). Any agent can also learn its strategy. In multitask learning, one agent studies a set of related problems together simultaneously, by a common model. In reinforcement learning exploration phase, it is necessary to introduce a process of trial and error to learn better rewards obtained from environment. To reach this end, anyone can typically employ the uniform pseudorandom number generator in exploration period. On the other hand, it is predictable that chaotic sources also offer a random-like series comparable to stochastic ones. It is useful in multitask reinforcement learning, to use teammate agents' experience by doing simple interactions between each other. We employ the past experiences of agents to enhance performance of multitask learning in a nondeterministic environment. Communications are created by operators of evolutionary algorithm. In this paper we have also employed the chaotic generator in the exploration phase of reinforcement learning in a nondeterministic maze problem. We obtained interesting results in the maze problem.
A thesaurus is a reference work that lists words grouped together according to similarity of meaning (containing synonyms and sometimes antonyms), in contrast to a dictionary, which contains definitions and pronunciations. This paper proposes an innovative approach to improve the classification performance of Persian texts considering a very large thesaurus. The paper proposes a flexible method to recognize and categorize the Persian texts employing a thesaurus as a helpful knowledge. In the corpus, when utilizing the thesaurus the method obtains a more representative set of wordfrequencies comparing to those obtained when the method disables the thesaurus. Two types of word relationships are considered in our used thesaurus. This is the first attempt to use a Persian thesaurus in the field of Persian information retrieval. The k-nearest neighbor classifier, decision tree classifier and k-means clustering algorithm are employed as classifier over the frequency based features. Experimental results indicate enabling thesaurus causes the method significantly outperforms in text classification and clustering.
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