@article {429,
	title = {Modeling Organizational Problem Solving in Multiagent Teams},
	number = {WP027},
	year = {1994},
	month = {02/1994},
	abstract = {Organizations involving human and computer agents are constrained by a variety of factors including: task properties and arrangements; level of technology; knowledge held by, and distributed among, the agents; information and administrative structures; and organizational norms and policies. An important challenge to the scientific community is to develop, validate and apply theories and models to help managers re-engineer their organizations for higher levels of performance. Our research on organizational problem solving aims to develop a computational model of organizations to study interrelationships between individual knowledge, task requirements and organization structures and policies. This paper reports the first step of our research toward a computational organizational model-the i-AGENTS framework, a prototype computer system for modeling organizations of intelligent agents. i-AGENTS is composed of a number of high level concepts: tasks, agents, organization and communication. A task is described in detail by task action, task object and task constraints; an agent is modeled to consist of cognitive attributes and expertise; role-based organizational structure is adopted for describing organizations. From an organizational perspective, i-AGENTS extends traditional information processing models of organization (Galbraith 77) by explicitly addressing the role of agents{\textquoteright} knowledge of both the problem domain and the organization in problem solving. When viewed from an engineering perspective, our research is the first step toward an organizational problem solving model that merges organization theory and distributed artificial intelligence and can be used to simulate and analyze organizational behavior of teams in engineering domains at a very specific level of detail.},
	keywords = {Computational Organizational Model, Multi-Agent Teams, Organizational Problem Solving},
	attachments = {http://cife.stanford.edu/sites/default/files/WP027.pdf},
	author = {Jin, Yan and Levitt, Raymond}
}
