this article. 0738-4602/92/$4.00 1992 AAAI 58 AI MAGAZINE for the 1990s (Silberschatz, Stonebraker, and Ullman 1990)

on parameterized complexity, and it hopefully serves as a driving force in the development of the eld. 1 We had 49 participants from Australia, Canada, India, Israel, New Zealand, USA, and various European countries. During the workshop 25 lectures were given. Moreover, one night session was devoted to open

This is a field report of a three-week experience in Japan, centered on art education in their cultural and social contexts. Beginning with this overarching focus, the themes and patterns that structure this report were emergent, rising from the experience. Those supporting themes are: being

Constraint Logic Programming (CLP) is a merger of two declarative paradigms: constraint solving and logic programming. Although a relatively new field, CLP has progressed in several quite different directions. In particular, the early fundamental concepts have been adapted to better serve in different areas of applications. In this survey of CLP, a primary goal is to give a systematic description of the major trends in terms of common fundamental concepts. The three main parts cover the theory, implementation issues, and programming for applications.

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SIS is an interactive tool for synthesis and optimization of sequential circuits. Given a state transition table, a signal transition graph, or a logic-level description of a sequential circuit, it produces an optimized net-list in the target technology while preserving the sequential input-output behavior. Many different programs and algorithms have been integrated into SIS, allowing the user to choose among a variety of techniques at each stage of the process. It is built on top of MISII [5] and includes all (combinational) optimization techniques therein as well as many enhancements. SIS serves as both a framework within which various algorithms can be tested and compared, and as a tool for automatic synthesis and optimization of sequential circuits. This paper provides an overview of SIS. The first part contains descriptions of the input specification, STG (state transition graph) manipulation, new logic optimization and verification algorithms, ASTG (asynchronous signal transition graph) manipulation, and synthesis for PGA’s (programmable gate arrays). The second part contains a tutorial example illustrating the design process using SIS.

We introduce a greedy local search procedure called GSAT for solving propositional satisfiability problems. Our experiments show that this procedure can be used to solve hard, randomly generated problems that are an order of magnitude larger than those that can be handled by more traditional approaches such as the Davis-Putnam procedure or resolution. We also show that GSAT can solve structured satisfiability problems quickly. In particular, we solve encodings of graph coloring problems, N-queens, and Boolean induction. General application strategies and limitations of the approach are also discussed. GSAT is best viewed as a model-finding procedure. Its good performance suggests that it may be advantageous to reformulate reasoning tasks that have traditionally been viewed as theorem-proving problems as model-finding tasks.

This paper describes an extension to the set of Basic Linear Algebra Subprograms. The extensions are targeted at matrix-vector operations which should provide for efficient and portable implementations of algorithms for high performance computers.

Active networks are a novel approach to network architecture in which the switches of the network perform customized computations on the messages flowing through them. This approach is motivated by both lead user applications, which perform user-driven computation at nodes within the network today, and the emergence of mobile code technologies that make dynamic network service innovation attainable. In this paper, we discuss two approaches to the realization of active networks and provide a snapshot of the current research issues and activities. Introduction – What Are Active Networks? In an active network, the routers or switches of the network perform customized computations on the messages flowing through them. For example, a user of an active network could send a “trace ” program to each router and arrange for the program to be executed when their packets are processed. Figure 1 illustrates how the routers of an IP