The distributed learning system : a group problem-solving approach to rule learning / 91-143
Riyaz T. Sikora, Michael Shaw · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign) · 1991
content of discussion (DeSanctis and Gallupe[1987]).Some examples of group problem solving may be organizational decision making where group of managers deliberate upon strategic planning, a creative group of people participating in a brainstorming session, a group of experts from different domains jointly design a product etc.One of the aims of this research is to address the issue: Under what conditions does a group problem-solving strategy, using multiple agents, perform better than a centralized, single-agent approach in a problem-solving situation?We tackle the above issue by extracting a set of core group parameters and group processes which have been identified by the GDSS and DAI research communities, and developing a computational model which can simulate such a group problem solving activity.Gallupe[1987]; Nunamaker et al. [1988]; George et al. [1990]) have identified a set of situational variables to be the most important in the design of GDSS: for the independent variables, group size, type of information exchange, member proximity, leadership, anonymity, and task confronting the group have been shown important for the dependent variables, decision outcomes (decision quality, number of unique alternatives generated, consensus), process outcomes (time to decision, participation, satisfaction with the process), and group coherence.Since we are concerned primarily in extracting features that might be relevant for both artificial as well as human agentsthat is, our aim is to study GPS as a computational process among problem-solving agents-we choose to ignore the behavioral factors such as proximity and satisfaction.In addition, research related to DAI (Bond and Gasser[1988]) has identified several important factors in designing DAI systems: Description, decomposition, distribution, and allocation of tasks; interaction, language, and communication; coherence and co-ordination; inter-agent disparities; synthesis of results.Based on these results, this research is aimed at studying the impacts of group size (number of agents), problem decomposition and task allocation, and diversity among the agents on the performance of the group, and developing a scheme for synthesizing the individual solutions of the agents. Several researchers in GDSS community (DeSanctis andRule induction was chosen as the problem domain of our study for its importance in automating the construction of knowledge-base systems from examples.The problem of rule induction can be simply stated as follows: based on a set of positive and negative examples of a concept infer a description or hypothesis of the concept which correctly explains all the positive examples without covering any of the negative examples.From the standpoint of McGrath's (1984) taxonomy of task types, rule induction may be characterized as a combination of intellective, creativity, and preference.As a result of its involvement of these multiple task types, it can potentially benefit from the use of group problem solving.The problem of rule learning or induction from examples is a very widely studied problem in the area of machine learning.Algorithms like Version-spaces (Mitchell[1977]), AQ(Michalski[1983]), ID3 (Quinlan[1986]), and PLSl(Rendell [1986]) are a few of the successful