The Association for the Advancement of Artificial Intelligence held its 1995 Spring Symposium Series on March 27 to 29 at Stanford University. This article contains summaries of the nine symposia that were conducted: (1) Empirical Methods in Discourse Interpretation and Generation; (2) Extending Theories of Action: Formal Theory and Practical Applications; (3) Information Gathering from Heterogeneous, Distributed Environments; (4) Integrated Planning Applications; (5) Interactive Story Systems: Plot and Character; (6) Lessons Learned from Implemented Software Architectures for Physical Agents; (7) Representation and Acquisition of Lexical Knowledge: Polysemy, Ambiguity, and Generativity; (8) Representing Mental States and Mechanisms; and (9) Systematic Methods of Scientific Discovery.

The human orientation system is a complex system in which the brain merges information from a variety of sensors to help maintain a coherent interpretation of body position and movement. I designed a model of this system based on the observer theory model (OTM), which was developed by Merfeld (1990) for the orientation system of the squirrel monkey. Under this scheme, the central nervous system has an internal representation of the sensor organs and tries to minimize the error between its estimate of the sensory afferent signals and the actual afferent signals. It works iteratively until the results of the proposed experiment can be modeled.

The human orientation system is a complex system in which the brain merges information from a variety of sensors to help maintain a coherent interpretation of body position and movement. These sensors include the semicircular canals and the otolith organs located in the inner ear as well as vision and somatosensory perception. I designed a model of this system based on the observer theory model (OTM), which was developed by Merfeld (1990) for the orientation system of the squirrel monkey. Under this scheme, the central nervous system has an internal representation of the sensor organs and tries to minimize the error between its estimate of the sensory afferent signals and the actual afferent signals. As designed, MARIKA's goal is to classify the vestibular system of the subject as normal or abnormal and propose a corresponding model. It works iteratively until the results of the proposed experiment can be modeled. Additional experiments can be presented in succession to the same model.

The central thesis of my dissertation (Kocabas 1989)1 is that in complex systems, descriptive and definitive knowledge can be organized into functional categories; this categorization provides clarity and efficiency in representation and facilitates the integrated use of various methods of learning. I describe a formalism for organizing knowledge into such functional categories and some of its implementations. In this formalism, descriptive scientific knowledge is classified into seven categories. The categorization formalism allows complex propositions to be analyzed into their simple constituents; in turn, these constituents can be maintained in their categories. They can then be combined using a simple transformation function to form complex constructs such as frames and schemata. The methodology facilitates the implementation of knowledge-level methods of learning such as similarity-based learning, explanation-based learning, and conceptual clustering. It simplifies the identification and resolution of conflicts in knowledge systems.

A fundamental question in AI is what mechanisms suffice for computer programs to make scientific discoveries. My Ph.D. thesis addresses this question by automating the following scientific task to a significant extent: Given observed data about a particular chemical reaction, discover the underlying set of reaction steps from starting materials to products, that is, elucidate the reaction pathway.

Hypothesis Formation and Qualitative Reasoning in Molecular Biology

My Ph.D. dissertation describes an investigation of scientific reasoning from a computational perspective.

A program called "AM" is described which carries on simple mathematics research,defining and studying new concepts under the guidance of a large body ofheuristic rules. The 250 heuristics communicate via an agenda mechanism, aglobal priority queue of small tasks for the program to perform, and reasons whyeach task is plausible (for example, "Find generalizations of 'primes', because'primes' turned out to be so useful a concept"). Each concept is represented asan active, structured knowledge module. One hundred very incomplete modulesare initially supplied, each one corresponding to an elementary set-theoreticconcept (for example, union). This provides a definite but immense space whichAM begins to explore. In one hour, AM rediscovers hundreds of common concepts(including singleton sets, natural numbers, arithmetic) and theorems (for example,unique factorization).Summary of Ph.D. dissertation.Hayes, J.E., D. Michie, and L. I. Mikulich (Eds.), Machine Intelligence 9, Ellis Horwood.

"Computing can change our ways of thinking about many things, mathematics, biology, engineering, administrative procedures, and many more. But my main concern is that it can change our thinking about ourselves: giving us new models, metaphors, and other thinking tools to aid our efforts to fathom the mysteries of the human mind and heart. The new discipline of Artificial Intelligence is the branch of computing most directly concerned with this revolution. By giving us new, deeper, insights into some of our inner processes, it changes our thinking about ourselves. It therefore changes some of our inner processes, and so changes what we are, like all social, technological and intellectual revolutions." This book, published in 1978 by Harvester Press and Humanities Press, has been out of print for many years, and is now online, produced from a scanned in copy of the original, digitised by OCR software and made available in September 2001. Since then a number of notes and corrections have been added. Atlantic Highlands, NJ: Humanities Press.

Retrospective on lessons learned from the Dendral project."The DENDRAL and Meta-DENDRAL programs are products of a large, interdisciplinary group of Stanford University scientists concerned with many and highly varied aspects of the mechanization of scientific reasoning and the formalization of scientific knowledge for this purpose. An early motivation for our wok was to explore the power of existing Al methods, such as heuristic search, for reasoning in difficult scientific problems. Another concern has been to exploit the AI methodology to understand better some fundamental questions in the philosophy of science, for example the processes by which explanatory hypotheses are discovered or judged adequate. From the start, the project has had an applications dimension. It has sought to develop "expert level" agents to assist in the solution of problems in their discipline that require complex symbolic reasoning. The applications dimension is the focus of this paper."Artificial Intelligence 11 (1-2): 5-24