AAAI Workshop on Cooperation Among Heterogeneous Intelligent Agents

AI Magazine

Recent attempts to develop larger and more complex knowledge-based systems have revealed the shortcomings and problems of centralized, single-agent architectures and have acted as a springboard for research in distributed AI (DAI). Although initial research efforts in DAI concentrated on issues relating to homogeneous systems (that is, systems using agents of a similar type or with similar knowledge), there is now increasing interest in systems comprised of heterogeneous components. The workshop on cooperation among heterogeneous intelligent agents, held July 15 during the 1991 National Conference on Artificial Intelligence, was organized by Evangelos Simoudis, Mark Adler, Michael Huhns, and Edmund Durfee. It was designed to bring together researchers and practitioners who are studying how to enable a heterogeneous collection of independent intelligent systems to cooperate in solving problems that require their combined abilities.


Robot Planning

AI Magazine

We can take planning to be the optimization and debugging of a robot's program by reasoning about possible courses of execution. It is necessary to the extent that fragments of robot programs are combined at run time. There are several strands of research in the field; I survey six: (1) attempts to avoid planning; (2) the design of flexible plan notations; (3) theories of time-constrained planning; (4) planning by projecting and repairing faulty plans; (5) motion planning; and (6) the learning of optimal behaviors from reinforcements. However, we are already beginning to see how to mesh plan execution with plan generation and learning.


Integrating Case-Based and Model-Based Reasoning: A Computational Model of Design Problem Solving

AI Magazine

My Ph.D. dissertation (Goel 1989) presents a computational model of experience-based design. It first reviews the core issues in experience-based design, for example, (1) the content of a design experience (or case), (2) the internal organization of design cases, (3) the language for indexing the cases, (4) the mechanism for retrieving a case relevant to a given design task, (5) the mechanism for adapting a retrieved design to satisfy the constraints of the design task, (6) the mechanism for evaluating a design against the specification of the design task, (7) the mechanism for redesigning a failed design, (8) the mechanism for acquiring new design knowledge, (9) the mechanism for chunking information about a design into a new case, and (10) the mechanism for storing a new case in memory for potential reuse in the future. It then proposes that decisions about these issues might lie in the designer's comprehension of the designs of artifacts he/she has encountered in the past, that is, in his/her mental models of how the designs achieve the functions and satisfy the constraints of the artifacts.


The Fourth International Symposium on Artificial Intelligence

AI Magazine

The Fourth International Symposium on Artificial Intelligence (ISAI) was held in Cancun, Mexico, 13-15 November 1991. What, another international AI conference, you say? The first symposium was held in 1988. This fourth consecutive annual conference drew the participation of visitors from several international AI communities, including the United States, Mexico, Canada, Germany, Japan, England, France, Italy, The Netherlands, Spain, China, Belgium, Australia, and Singapore -- an impressive breadth of participants for a conference that has existed for only four years.



The Fourth International Symposium on Artificial Intelligence

AI Magazine

The Fourth International Symposium on Artificial Intelligence (ISAI) was held in Cancun, Mexico, 13-15 November 1991. What, another international AI conference, you say? In Mexico? Yes. The first symposium was held in 1988. This fourth consecutive annual conference drew the participation of visitors from several international AI communities, including the United States, Mexico, Canada, Germany, Japan, England, France, Italy, The Netherlands, Spain, China, Belgium, Australia, and Singapore -- an impressive breadth of participants for a conference that has existed for only four years.


Integrating Case-Based and Model-Based Reasoning: A Computational Model of Design Problem Solving

AI Magazine

My Ph.D. dissertation (Goel 1989) presents a computational model of experience-based design. It first reviews the core issues in experience-based design, for example, (1) the content of a design experience (or case), (2) the internal organization of design cases, (3) the language for indexing the cases, (4) the mechanism for retrieving a case relevant to a given design task, (5) the mechanism for adapting a retrieved design to satisfy the constraints of the design task, (6) the mechanism for evaluating a design against the specification of the design task, (7) the mechanism for redesigning a failed design, (8) the mechanism for acquiring new design knowledge, (9) the mechanism for chunking information about a design into a new case, and (10) the mechanism for storing a new case in memory for potential reuse in the future. It then proposes that decisions about these issues might lie in the designer's comprehension of the designs of artifacts he/she has encountered in the past, that is, in his/her mental models of how the designs achieve the functions and satisfy the constraints of the artifacts.


Cognitively Plausible Heuristics to Tackle the Computational Complexity of Abductive Reasoning

AI Magazine

The work described in my Ph.D. dissertation (Fischer 1991)1 merges computational and cognitive investigations of abductive reasoning. It is the outcome of seven years of research focusing on abductive explanation generation and involving the departments of computer and information science, industrial and systems engineering, pathology, and allied medical professions at The Ohio State University.



Editorial: Ontogeny Recapitulates Ontegeny: AI and AI Magazine

AI Magazine

As the AI community has matured, the role of AI Magazine has continued to evolve. Rich outlines several ways that this community-wide publication can address the current needs of AI researchers, and encourages broad participation from community members.