AI Magazine Volume 10 Number 4 (1989) ( AAAI) generated some controversy. Relative to the discussion of the role of strong syllables in lexical segmentation, Gerry Altmann of CSTR reviewed some of the evidence based on computational studies of large The 1988 Workshop on Cognitive bone. Evidence from human studies computerized lexicons (20,000 Models of Speech Processing was suggested that the spurious word is words). This evidence suggested that held at Park Hotel Fiorelle, Sperlonga, activated, even though in principle it a stressed syllable conveys more Italy, on 16-20 May 1988. Twentyfive would be possible to prevent this activation information about the identity of the participants gathered in this by only accessing the lexicon at word in which it occurs than an small coastal village, where the the offset of some previously found unstressed syllable.

The vendors Based Systems, 355 pp., and Volume 2, techniques. They are interesting of knowledge-based-systems development Knowledge Acquisition Tools for Expert and informative, particularly tools, for example, Inference, Systems, 343 pp., Academic Press, San "Generalization and Noise" by Y. IntelliCorp, Aion, AI Corp., and IBM, Diego, California, 1988), edited by B. Kodratoff and M. Manango, which would do well to pay heed to these R. Gaines and J. H. Boose, is an excellent discusses symbolic and numeric rule books because they point the way to collection of papers useful to both induction.

Since this article is a counting argument, the conclusion time, a number of alternatives have been proposed. Presumably, in realistic cases, the Universally Bad Idea," analyzes one such number of sensors is large enough that a universal alternative, Marcel Schoppers's universal plan could not fit in your head. He also extends this analysis to a There are two reasons not to be concerned number of other systems, including Pengi about this apparent problem. They involve (A gre and Chapman 1987), which was structure and state, designed by Phil Agre and myself. Ginsberg's criticisms of universal plans rest Using universal plans, he says, is infeasible because their size is exponential in the number of possible domain states. Representing such a plan is infeasible in even quite small realistic domains. I'm sympathetic to such arguments, having made similar ones to the effect that classical planning is infeasible (Agre and Chapman 1988; Chapman 1987b). I don't understand the details of Schoppers's ideas, so I'm not sure whether this critique of universal plans per se is correct. However, I show that these arguments do not extend to Pengi. Ginsberg calls Pengi an approximate universal plan, by which he means it is like a universal plan except that it does not correctly specify what to do in every situation. However, Pengi's operation involves no plans, universal or approximate, and Pengi and universal plans, although they share some motivations, have little to do with each other as technical proposals. Ginsberg suggests number of its inputs. Pengi-like system, computation in the number of pixels or that, Blockhead, which efficiently solves the fruitcake on the average, business data processing takes problem; the way it solves it elucidates exponential work in the number of records. They have a lot The fruitcake problem is to stack a set of of structure to them, and this structure can be labeled blocks so that they spell the word exploited to exponentially reduce the computation's fruitcake. What is apparently difficult about size. I show impossible under the rules of the domain, Blockhead solving a problem involving 45 and the remainder can be categorized relatively blocks in which there are 45! 1056 configurations, cheaply to permit abstraction and There is every in every configuration, so it is not by reason to think that this same structure is approximation that it succeeds. Indeed, Ginsberg makes this and a central system. The [planning couldn't work if] there were no visual system is a small subset of Pengi's rhyme or reason to things."

The Advanced Architectures Project at Stanford University's Knowledge Systems Laboratory seeks to gain higher performance for expert system applications through the design of new, innovative software and hardware architectures. This research concentrates particularly on the use of parallel machines to gain speedup and the design of the software to exploit emergent paral-lel hardware architectures. This article describes the project and details its goals and the work performed in the pursuance of these goals. A brief description is given of each of the project components, and a complete bibliography appears of the publications produced for the project.

Second, These sections describe how Phoenix agents there are motivating issues, of plan in real time but do not provide the which the foremost is to understand minute detail that is offered elsewhere (Cohen how complex environments et al. forthcoming). The next section illustrates constrain on the design of Phoenix agents controlling a forest fire. We seek general The last section describes the current status of rules that justify and explain the project and our immediate goals. The terms in these rules describe The Phoenix task is to control simulated characteristics of environments, forest fires by deploying simulated bulldozers, tasks and behaviors, and the crews, airplanes, and other objects. We discuss architectures of agents. Phoenix Environment, Layers 1 and 2 but Phoenix is a commentary on the Phoenix Simulator. In the following pages, we describe Phoenix from the perspective of our technical aims and motives. The second section describes the Phoenix task--controlling simulated forest fires-- and explains why we use a simulated environment instead of a real, physical one. The two lowest layers of Phoenix, described in The Phoenix Environment, Layers 1 and 2, implement the simulated environment and maintain the illusion that the forest fire and agents are acting simultaneously. Above these layers are two others: a Figure 2. Fire at 12:30 Bulldozers are Close to organization of multiple Meeting at the Fire Front. The left pane displays the real world; the right pane displays fireboss sees it. Firefighting objects are also and other agents are semiautonomous.

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.

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.

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.