Advances in computer hardware and software and engineering methodologies in the 1960s and 1970s led to an increased use of computers by engineers. However, a number of problems encountered in design are not amenable to purely algorithmic solutions. In this article, we describe several research projects that utilize KBS techniques for design automation. These projects are (1) the Criteria Yielding, Consistent Labeling with Optimization and Precedents-Based System (CYCLOPS), which generates innovative designs by using a three-stage process: normal search, exploration, and adaptation; (2) the Concept Generator (CONGEN), which is a domain independent framework for conceptual or preliminary design; (3) Constraint Manager (CONMAN), which is a constraint-management system that performs the evaluation and consistency maintenance of constraints arising in design; (4) the distributed and integrated environment for computer-aided engineering (DICE), which facilitates coordination, communication, and control during the entire design and construction/manufacturing phases; and (5) DESIGN-KIT, which can be envisioned as a new generation of computer-aided engineering environment for processengineering applications. The types of problems that engineers normally solve are bounded by the derivationformation spectrum.

In both individual and collective decision making, the space of alternatives from which the agent (or the group of agents) has to choose often has a combinatorial (or multiattribute) structure. We give an introduction to preference handling in combinatorial do - mains in the context of collective decision making and show that the considerable body of work on preference representation and elicitation that AI researchers have been working on for several years is particularly relevant. After giving an overview of languages for compact representation of preferences, we discuss problems in voting in combinatorial domains and then focus on multiagent resource allocation and fair division. These issues belong to a larger field, which is known as computational social choice and which brings together ideas from AI and social choice theory, to investigate mechanisms for collective decision making from a computational point of view. We conclude by briefly describing some of the other research topics studied in computational social choice.

Such false-name manipulations have traditionally not been considered in the theory of mechanism design. In this article, we review recent efforts to extend the theory to address this. Because some of these results are very negative, we also discuss alternative models that allow us to circumvent some of these negative results. Some of the most exciting applications of this involve making decisions based on the agents' preferences (for a more detailed discussion, see Conitzer [2010]). For example, in electronic commerce, agents can bid on items in online auctions.

Introduction One of the major deterrents to productivity in industry today is the inability to effectively manage and control production. The problem is particularly acute in job shop environments where plant operation is routinely characterized by high work-in-process (WIP) inventories, tardy orders, poor resource utilization, and other shop floor inefficiencies. Perhaps the single most significant obstacle to improved factory performance is the complexity associated with constructing and maintaining good production schedules. Good schedules must reflect both the full detail of the operating environment and the influence of a conflicting set of preferences that range from global organizational objectives to specific operational idiosyncrasies. Existing computer-based techniques for production scheduling are capable of incorporating only a small fraction of this scheduling knowledge and, as a result, typically produce schedules that bear little resemblance to the actual state of the ...

This article provides an overview of the field of qualitative decision theory: its motivating tasks and issues, its antecedents, and its prospects. Qualitative decision theory studies qualitative approaches to problems of decision making and their sound and effective reconciliation and integration with quantitative approaches. Although it inherits from a long tradition, the field offers a new focus on a number of important unanswered questions of common concern to AI, economics, law, psychology, and management. As developed by philosophers, economists, and mathematicians over some 300 years, these disciplines have developed many powerful ideas and techniques, which exert major influences over virtually all the biological, cognitive, and social sciences. Their uses range from providing mathematical foundations for microeconomics to daily application in a range of fields of practice, including finance, public policy, medicine, and now even automated device diagnosis.

We propose an approach for meeting realtime constraints in AI systems that views (1) time as a resource that should be considered when making control decisions, (2) plans as ways of expressing control decisions, and (3) approximate processing as a way of satisfying time constraints that cannot be achieved through normal processing. In this approach, a real-time problem solver estimates the time required to generate solutions and their quality. This estimate permits the system to anticipate whether the current objectives will be met in time. The system can then take corrective actions and form lower-quality solutions within the time constraints. These actions can involve modifying existing plans or forming radically different plans that utilize only rough data characteristics and approximate knowledge to achieve a desired speedup.

Introduction This article describes the initial stages of an effort to develop a knowledge-based financial marketing consultant system. The project for Financial Marketing Expertise (FAME), is to produce a system that addresses the area usually referred to asfinnncial nzarketing. This term characterizes the financial decision processes used in the marketing of products and services of such large scale that they can significantly impact a company's financial status. In particular, our project emphasizes financial marketing as it applies to the marketing of computers. For instance, a customer interested in buying computing technology on a large scale is usually concerned that the financing plan being used to acquire the technology is safe, sound, and attractive from a financial investment point of view. Therefore, in making very large sales, financial considerations often become as important as the computing considerations. We have found financial marketing to be a very interesting and ...

We read a lot of news about chatbots reshaping entire industry sectors by utilizing artificial intelligence (AI), machine learning and natural language processing. Some chatbots are good in assisting consumers in buying tickets or finding good food nearby. Others can keep a simple conversation alive or replace traditional FAQ pages. What most media pundits miss, however, is that such a capability is nowhere near general AI potential, which casts doubts over the very future of chatbots. For start, chatbots are of two basic types: conversational and goal-oriented.

This article picks up the call for a reflective examination of the prevailing computational metaphor of AI (and philosophical presuppositions behind it) by sketching alternatives that might serve as seeds for discussion-specifically, the seven alternatives introduced in our previous article (see "AI Magazine, spring 1991). The relative strengths and weaknesses of the alternatives are contrasted with those of the computational metaphor.