This Special Issue had its genesis in an international Workshop on Agents in Design held in June 2002, at MIT by the Guest Editors. Computational agents have been developed within the artificial intelligence community over an extended period. The concept of an agent can be traced to Carl Hewitt's 1977 work on “actors.” Hewitt defined actors as self-contained, interactive, and concurrently executing objects. Since then, considerable research has gone into developing the concept of an agent and into formalizing agents, developing multiagent systems, and exploring their use. The use of agents in design is more recent, and the first PhDs in the area appeared in the early 1990s. Although a precise and unique definition of an agent has yet to be agreed upon, one distinguishing characteristic of an agent is that it exhibits autonomous behavior. Research on agents in design focuses on two primary areas: how to make agents useful in design, and how to apply them to design tasks. This Special Issue has papers from both areas.

An innovative approach has been developed for discovering better design methodologies that is based on simulating the design process using a multiagent system that mimics the behavior of a design team. The system implements a knowledge-based model of design in which highly specialized knowledge from expert sources is applied to synthesize a design. The agents activate the pieces of design knowledge when they become applicable. The use of knowledge by agents is recorded by tracing the steps that the agents have taken during a design project. Many traces are generated by solving a large number of design projects that differ in their requirements. A set of design methodologies is constructed by using inductive learning techniques to generalize the traces generated. These methodologies then can be used to guide human design teams through future design projects.

Organizational research has shown that effectively structuring the resources (human, informational, computational) available to an organization can significantly improve its collective computational capacity. Central to this improved capacity is the manner in which the organization's member agents are related. This study is an initial investigation into the role and potential of interagent ties in computational teaming. A computational team-based model, designed to more fully integrate agent ties, is created and presented. It is applied to a bulk manufacturing process-planning problem and its performance compared against a previously tested agent-based algorithm without these agent relationships. The performance of the new agent method showed significant improvement over the previous method: improving solution quality 280% and increasing solution identification per unit time an entire order of magnitude. A statistical examination of the new algorithm confirms that agent interdependencies are the strongest and most consistent performance effects leading to the observed improvements. This study illustrates that the interagent ties associated with team collaboration can be a highly effective method of improving computational design performance, and the results are promising indications that the application of organization constructs within a computational context may significantly improve computational problem solving.

This paper presents a possible future direction for agent-based simulation using complex agents that can learn from experience and report their individual evaluations. Adding learning to the agent model permits the simulation of potentially important agent behavior such as curiosity. The agents can then report evaluations of a design that are situated in their individual experience. The paper describes the architecture of curious agents used in the situated evaluation of designs. It then describes an example of the application of such curious agents in the evaluation of the curating of an exhibition in an art gallery.

Design is situated. “Situatedness” in designing entails the explicit consideration of the state of the environment, the knowledge and experiences of the designer, and the interactions between the designer and the environment during designing. Central to the notion of situatedness is the notion of design situation and constructive memory. A design situation models a particular state of interaction between a design agent and the environment at a particular point in time. Memory construction occurs whenever a design agent uses past experiences and knowledge within the current design environment in a situated manner. This paper is concerned with the development of an agent-based computational design tool that takes into consideration the notion of situatedness in designing. A key element of this tool is a constructive memory system that supports the dynamic nature of designing. Memories of past experiences are constructed as required by the current situation, and past experiences are refined for future utility according to the current interactions between the agent and the environment. This latter case of knowledge improvement is illustrated through a series of experiments that demonstrates the effect of grounding on the operating modes and responses of a constructive memory system.

Automated support of design teams, consisting of both human and automated systems, requires an understanding of the role of trust in distributed design processes. By explicating trust, an individual designer's decisions become better understood and may be better supported. Each individual designer has his or her private goals in a cooperative design setting, in which requirement conflicts and resource competitions abound. However, there are group goals that also need to be reached. This paper presents an overview of research related to trust in the context of agents and design, a computational knowledge-level model of trust based on the seven beliefs distinguished by Castelfranchi and Falcone, and an example of the use of the trust model in a specific design process, namely, Website design from the perspective of a single designer. The results are discussed in the context of distributed design in open systems.