Computation and Intelligence: Collected Readings
George F. Luger
AAAI Press, 1995 - Computers - 735 pages
Computation and Intelligence brings together 29 readings in Artificial Intelligence that are particularly relevant to today's student/practitioner. With its helpful critique of the selections, extensive bibliography, and clear presentation of the material, Computation and Intelligence will be a useful adjunct to any course in AI as well as a handy reference for professionals in the field.
The book is divided into five parts, each reflecting the stages of development of AI. The first part, Foundations,, contains readings that present or discuss foundational ideas linking computation and intelligence, typified by A. M. Turing's "Computing Machinery and Intelligence." The second part, Knowledge Representation, presents a sampling of numerous representational schemes by Newell, Minsky, Collins & Quillian, Winograd, Schank, Hayes, Holland, McClelland, Rumelhart, Hinton, and Brooks.
The third part, Weak Method Problem Solving, fouses on the research and design of syntax-based problem solvers, including the most famous of these, the Logic Theorist and GPS. The fourth part, Reasoning in Complex and Dynamic Environments, presents a broad spectrum of the AI community's research in knowledge-intensive problem solving, from McCarthy's early design of systems with "common sense" to model-based reasoning.
The two concluding selections, by Marvin Minsky and by Herbert Simon, respectively, present the recent thoughts of two of AI's pioneers who revisit the concepts and controversies that have developed during the evolution of the tools and techniques that make up the current practice of Artificial Intelligence.
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A sample MYCIN rule. one another before a consultation begins; the program
selects the relevant rules and chains them together as it considers a particular
patient. Two rules chain together if the action portion of one helps determine the
truth value of a condition in the premise of the other. The resulting reasoning
network, then, is created dynamically and can be seen as a model of one
approach to the patient's problem. MYCIN's strategy in rule selection is goal-
oriented. The program ...
In Figure 3, the form of MYCIN's final conclusions and therapy recommendations
are demonstrated. Note that the program specifies what organisms are likely to
be causing the patient's infection and then suggests a therapeutic regimen
appropriate for them. There are also specialized routines to calculate
recommended drug doses depending upon the patient's size and kidney function
. 5.2.2. Management of Uncertainty The knowledge expressed in a MYCIN rule is
seldom definite but ...
Each additional "WHY" is interpreted by MYCIN as a request for display of the
next rule in the current reasoning chain. For example, in Figure 4 another "WHY"
would be equivalent to asking, "Well then, why are you trying to determine the
organisms which might be causing the infection?" After responding to each "WHY
," MYCIN returns to the current question and awaits the physician's response. The
"HOW" command is used in conjunction with the "WHY" command. Note that
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given a world of three statement A,B,C write statement "Almaz is student" who has a pen has pencil using the predicate student(x) to mean x is student and has (x,y) to mean x has y
Computing Machinery and Intelligence
Part TwoKnowledge Representation
A Framework for Representing Knowledge
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