No description available.

This study serves two aims: (1) to evaluate the results of role playing on relevant criteria, and (2) to evaluate these same results from the standpoint of a particular frame of reference, namely that of religious education.

No description available.

Thesis (Ph. D.)--Open University. BLDSC no. DX217371.

published_or_final_version / Psychology / Master / Master of Philosophy

No description available.

No description available.

The present research addressed three major role-taking issues: (1) its developmental nature; (2) its underlying variables; and (3) its status within social cognition. Children between 6 and 11 years were tested. Study 1 examined the development of 3 role-taking tasks. Study 2 compared role taking to nonsocial word-pair comparison to determine whether the self's involvement demanded additional skills. Using original tasks, Studies 3 and 4 related role taking and comparison to referential communication. Study 4 also examined (a) the effects on these behaviours of a direct attentional decentration manipulation and (b) their relation to integration as measured by a modified Gergen-Morse Perceived Self-Consistency Scale. Major results indicated that: most children developed role taking around age 6 and mastered it by 11; nonsocial decentration (comparison) was related to role taking, with comparison developing first; and integration related to role taking but not to comparison. Finally, comparison and role taking were necessary but insufficient for referential communication; integration was also related to communication. The implications of these findings for a theory of role taking were discussed.

Domain-independent feature learning is a hard problem. This is reflected by lack of broad research in the area. The goal of General Game Playing (GGP) can be described as designing computer programs that can play a variety of games given only a logical game description. Any learning has to be domain-independent in the GGP framework. Learning algorithms have not been an essential part of all successful GGP programs. This thesis presents a feature learning approach, GIFL, for 2-player, alternating move games using state differences. The algorithm is simple, robust and improves the quality of play. GIFL is implemented in a GGP program, Maligne. The experiments show that GIFL outperforms standard UCT algorithm in nine out of fifteen games and loses performance only in one game.

Thesis (D. Min.)--Trinity Evangelical Divinity School, 1990. / Abstract. Includes bibliographical references (leaves 151-152).