The user interface to an expert system shares many design objectives and methods with the interface to a computer system of any sort. Nevertheless, significant aspects of behavior and user expectation are peculiar to expert systems and their users. These considerations are discussed here with examples from an actual system. Guidelines for the behavior of expert systems and the responsibility of designers to their users are proposed. Simplicity is highly recommended. Entia non sunt multiplicanda praete necessitatem.

Commonsense reasoning about the physical world, as exemplified by "Iron sinks in water" or "If a ball is dropped it gains speed," will be indispensable in future programs. We argue that to make such predictions (namely, envisioning), programs should use abstract entities (such as the gravitational field), principles (such as the principle of superposition), and laws (such as the conservation of energy) of physics for representation and reasoning. These arguments are in accord with a recent study in physics instruction where expert problem solving is related to the construction of physical representations that contain fictitious, imagined entities such as forces and momenta (Larkin 1983). We give several examples showing the power of physical representations.

McDermott makes some valid points in his review ... It's too bad that he chose to embed the helpful comments in the context of his by-now-tiresome doubts about the value of logic in AI.

Largely from this Traditionally, systems that automatically and realization--was widely experience, we came to understand generate natural language have deemed more convenient than accurate: the sorts of tasks that a text planner been conceived as consisting of two The components of a generator has to perform: determining which principal components: a text planner should be able to communicate at elements to say, coherently structuring and a realization grammar. Recent any level where their information is the input elements, building advances in the art, especially in the applicable.

The A.T. Nonmonotonic Workshop multisubmission paper policy by Anderson Memorial Scholarship Program The third international workshop on IJCAI was deferred until the of the American Indian Science August meeting.

To read the book "Automated Reasoning: Thirty-Three Basic Research problems (Prentice Hall, Englewood Cliffs, N.J., 1987, 300 pp., $11.00) by Larry Was it is not necessary to be an expert in mathematics or logic or computer science. However, even if you are such an expert, you will read it with interest, and likely, with enjoyment.

The book "Logical Foundations of Artificial Intelligence (Morgan Kaufmann, Los Altos, Calif., 1987, 406 pp., $48.95) by Michael Genesereth and Nils Nilsson is about declarative knowledge.

These debates end by a culture for accommodating of the medical AI community, I feel I up merely as arguments in which its limited knowledge representations. Those of us in intelligence is). Depending such an extent that the limits of the medical AI have been highly sensitized upon what properties of human and computer system would no longer be to common misunderstandings artificial intelligence are stressed we a representational problem? We also encounter a general lack of of the relationship. Will we need to ascribe pleasure and realistic expectations regarding the The problem is that the models of pain to our computer experts?

This article is a summary of the Workshop on Explanation held during the 1988 National Conference on Artificial Intelligence in St. Paul, Minnesota. The purpose of the workshop was to identify key research issues in the rapidly emerging area of expert system explanation.

Problem-solving techniques such as modeling, simulation, optimization, and network analysis have been used extensively to help agricultural scientists and practitioners understand and control biological systems. By their nature, most of these systems are difficult to quantitatively define. Many of the models and simulations that have been developed lack a user interface which enables people other than the developer to use them. As a result, several scientists are integrating knowledge-based- system (KBS) technology with conventional problem-solving techniques to increase the robustness and usability of their systems. To investigate the similarities and differences of leading scientists' approaches, a pioneer workshop, supported by the Association for the Advancement of Artificial Intelligence (AAAI) and the Knowledge Systems Area of the American Society of Agricultural Engineers, was held in San Antonio, Texas, on 10-12 August 1988. Part of the AAAI Applied Workshop Series, the meeting was intended to bring together researchers and practitioners active in applying AI concepts to agricultural problems.