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.

This article explores the premise that a formalized representation of empirical studies can play a central role in computer- based decision support. The specific motivations underlying this research include the following propositions: (1) Reasoning from experimental evidence contained in the clinical literature is central to the decisions physicians make in patient care. (2) A computational model based on a declarative representation for published reports of clinical studies can drive a computer program that selectively tailors knowledge of the clinical literature as it is applied to a particular case. (3) The development of such a computational model is an important first step toward filling a void in computer-based decision support systems. Furthermore, the model can help us better understand the general principles of reasoning from experimental evidence both in medicine and other domains. Roundsman is a developmental computer system that draws on structured representations of the clinical literature to critique plans for the management of primary breast cancer. Roundsman is able to produce patient-specific analyses of breast cancer-management options based on the 24 clinical studies currently encoded in its knowledge base. The Roundsman system is a first step in exploring how the computer can help bring a critical analysis of the relevant literature, structured around a particular patient and treatment decision, to the physician.

The notion of generic design, although it has been around for 25 years, is not often articulated; such is especially true within Newell and Simon's (1972) information-processing theory (IPT) framework. Design is merely lumped in with other forms of problem-solving activity. Intuitively, one feels there should be a level of description of the phenomenon that refines this broad classification by further distinguishing between design and nondesign problem solving. However, IPT does not facilitate such problem classification. This article makes a preliminary attempt to differentiate design problem solving from nondesign problem solving by identifying major invariants in the design problem space.

Configuring N mutually nonattacking queens on an N-by-N chessboard is a classical problem that was first posed over a century ago. Over the past few decades, this problem has become important to computer scientists by serving as the standard example of a globally constrained problem which is solvable using backtracking search methods. A related problem, placing the N queens on a toroidal board, has been discussed in detail by Poyla and Chandra. Their work focused on characterizing the solvable cases and finding solutions which arrange the queens in a regular pattern. This paper describes a new divide-and-conquer algorithm that solves both problems and investigates the relationship between them.

Artificial intelligence has progressed to the point where multiple cognitive capabilities are being integrated into computational architectures, such as SOAR, PRODIGY, THEO, and ICARUS. Learning in PRODIGY occurs at all decision points and integration in PRODIGY is at the knowledge level; the learning and reasoning modules produce mutually interpretable knowledge structures. Issues in architectural design are discussed, providing a context to examine the underlying tenets of the PRODIGY architecture.

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Proc. Eleventh Intl. Joint Conference on Artificial Intelligence (pp. 731-737). Detroit, MI: IJCAII.

In IJCAI-89, pp. 334–340.

Recently, many modifications to the McCulloch/Pitts model have been proposed where both learning and forgetting occur. Given that the network never saturates (ceases to function effectively due to an overload of information), the learning updates can continue indefinitely. For these networks, we need to introduce performance measmes in addition to the information capacity to evaluate the different networks. We mathematically define quantities such as the plasticity of a network, the efficacy of an information vector, and the probability of network saturation. From these quantities we analytically compare different networks.

LEARNING BY ST ATE RECURRENCE DETECfION Bruce E. Rosen, James M. Goodwint, and Jacques J. Vidal University of California, Los Angeles, Ca. 90024 ABSTRACT This research investigates a new technique for unsupervised learning of nonlinear control problems. The approach is applied both to Michie and Chambers BOXES algorithm and to Barto, Sutton and Anderson's extension, the ASE/ACE system, and has significantly improved the convergence rate of stochastically based learning automata. Recurrence learning is a new nonlinear reward-penalty algorithm. It exploits information found during learning trials to reinforce decisions resulting in the recurrence of nonfailing states. Recurrence learning applies positive reinforcement during the exploration of the search space, whereas in the BOXES or ASE algorithms, only negative weight reinforcement is applied, and then only on failure. Simulation results show that the added information from recurrence learning increases the learning rate. Our empirical results show that recurrence learning is faster than both basic failure driven learning and failure prediction methods. Although recurrence learning has only been tested in failure driven experiments, there are goal directed learning applications where detection of recurring oscillations may provide useful information that reduces the learning time by applying negative, instead of positive reinforcement.