This report contains the reading list for the Qualifying Examination in Artificial Intelligence. Areas covered include search, representation, reasoning, planning and problem solving, learning, expert systems, vision, robotics, natural language, perspectives and AI programming. An extensive bibliography is also provided.

Hillis's book, like its bibliography, has something to offer nearly everyone in the computer science community, even those with no background in computer architecture. The book provides a glimpse of what the next generation of computers will be like.

Cambridge, can read this material and gain insight into some of the Massachusetts: The MIT Press, 1985. The treatment is not detailed enough to be used as a text on The Connection Machine introduces a new type of parallel architecture design but it is illuminating and interesting computer which may lead to radically new ways of to read. Once the reader has been introduced to the basics of The author, Daniel Hillis, is the designer of the Connection Machine architecture, the author presents a machine and the founder of Thinking Machines Corporation, description of a prototype called CMl; a machine with a company committed to building "Connection Machines." Hillis discusses the Hillis' book describes the Connection Machine custom VLSI chip, details of the simple processor cells, and and the issues surrounding its design. At made up of thousands, potentially millions, of small, simple, times the Connection Machine appears so different from processors working simultaneously, each with its own current computers that it seems more akin to science fiction tiny memory.

AI Magazine, 6(4):65-85

Cambridge, Mass.: MIT Press

Cooperative distributed problem solving networks are distributed networks of semi-autonomous processing nodes that work together to solve a single problem. The Distributed Vehicle Monitoring Testbed is a flexible and fully-instrumented research tool for empirically evaluating alternative designs for these networks. The testbed simulates a class of a distributed knowledge-based problem solving systems operating on an abstracted version of a vehicle monitoring task. There are two important aspects to the testbed: (1.) it implements a novel generic architecture for distributed problems solving networks that exploits the use of sophisticated local node control and meta-level control to improve global coherence in network problem solving; (2.) it serves as an example of how a testbed can be engineered to permit the empirical exploration of design issues in knowledge AI systems. The testbed is capable of simulating different degrees of sophistication in problem solving knowledge and focus-of attention mechanisms, for varying the distribution and characteristics of error in its (simulated) input data, and for measuring the progress of problem solving. Node configuration and communication channel characteristics can also be independently varied in the simulated network.

"We describe the concept of distributed problem solving and define it as the cooperative solution of problems by a decentralized and loosely coupled collection of problem solvers. This approach to problem solving offers the promise of increased performance and provides a useful medium for exploring and developing new problem-solving techniques. We present a framework called the contract net that specifies communication and control in a distributed problem solver. Task distribution is viewed as an interactive process, a discussion carried on between a node with a task to be executed and a group of nodes that may be able to execute the task. We describe the kinds of information that must be passed between nodes during the discussion in order to obtain effective problem-solving behavior. This discussion is the origin of the negotiation metaphor: Task distribution is viewed as a form of contract negotiation. We emphasize that protocols for distributed problem solving should help determine the content of the information transmitted, rather than simply provide a means of sending bits from one node to another. The use of the contract net framework is demonstrated in the solution of a simulated problem in area surveillance, of the sort encountered in ship or air traffic control. We discuss the mode of operation of a distributed sensing system, a network of nodes extending throughout a relatively large geographic area, whose primary aim is the formation of a dynamic map of traffic in the area. From the results of this preliminary study we abstract features of the framework applicable to problem solving in general, examining in particular transfer of control. Comparisons with PLANNER, CONNIVER, HEARSAY-II, AND PUP6 are used to demonstrate that negotiation—the two-way transfer of information—is a natural extension to the transfer of control mechanisms used in earlier problem-solving systems." Artificial Intelligence 20:63-109.

"Two forms of cooperation in distributed problem solving are considered: task-sharing and result-sharing. In the former, nodes assist each other by sharing the computational load for the execution of subtasks of the overall problem. In the latter, nodes assist each other by sharing partial results which are based on somewhat different perspectives on the overall problem. Different perspectives arise because the nodes use different knowledge sources (KS’s) (e.g., syntax versus acoustics in the case of a speech-understanding system) or different data (e.g., data that is sensed at different locations in the case of a distributed sensing system). Particular attention is given to control and to internode communication for the two forms of cooperation. For each, the basic methodology is presented and systems in which it has been used are described. The two forms are then compared and the types of applications for which they are suitable are considered." IEEE Transactions on Systems, Man, and Cybernetics, SMCll(l):61-70. PDF: http://www.reidgsmith.com/Frameworks_for_Cooperation_in_Distributed_Problem_Solving_Jan-1981.pdf.