Cohen, P., Feigenbaum, E., Eds.: The Handbook of Artificial Intelligence, vol. 3, Morgan Kaufmann, 1981.  Home/Search   Document Not in Database   Summary   Related Articles   Check

.... are reported in [19] In the relational database field various algorithms for deriving schemas in normal form have been developed to help the application builder to pin point design flaws [44] The problem of learning classes from object examples has been studied earlier in AI (e.g. 62] [16]) Clustering algorithms have been applied to build a tree of mutually exclusive classes from a given object set. Data models exist in the literature that are similar to the Demeter model used in this thesis. In particular, the notions of alternation and construction appear as classification ....

Paul R. Cohen and Edward A. Feigenbaum. The Handbook of Artificial Intelligence, volume 3. William Kaufmann, Inc., 1982.

..... Decision rule an assertion that determines the cause effect relationship between conditions and decisions. The three types of rules are useful in various applications of data mining. To enlarge the scope of their applicability, the learned rules can be generalized using the following methods [2]: replacing fixed values with intervals; e.g. rule IF a in [2, 7] THEN D = 1 is more general than the rule IF a = 3 then D = 1; deleting conjunctive elements; e.g. rule IF a = 3 THEN D = 1 is more general than the rule IF a = 3 AND b = 4 then D = 1; incorporating disjunctive elements; ....

....between conditions and decisions. The three types of rules are useful in various applications of data mining. To enlarge the scope of their applicability, the learned rules can be generalized using the following methods [2] replacing fixed values with intervals; e.g. rule IF a in [2, 7] THEN D = 1 is more general than the rule IF a = 3 then D = 1; deleting conjunctive elements; e.g. rule IF a = 3 THEN D = 1 is more general than the rule IF a = 3 AND b = 4 then D = 1; incorporating disjunctive elements; e.g. rule IF a = 3 OR b = 5 THEN D = 1 is more general than the rule ....

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P. Cohen and E. A. Feigenbaum, "The handbook of artificial intelligence, " in The Handbook of Artificial Intelligence. San Mateo, CA: Morgan Kaufmann, 1983, vol. 3.

....other measures to sort the output (e.g. cross citation index, usage frequency, user recommendations . 2. Show additional information to enable the user to browse the list faster. In computer vision labelling techniques and algorithms have a long tradition for constraint propagation (e.g. Cohen, Feigenbaum (Eds. 1982)) or for labelling image parts (e.g. Leung, Yang (1995) However, in this paper we understand labelling as a visualization technique for Internet resources. Section 2 describes the labelling process. Next, we discuss several dimensions of labelling like time, region, language, rating and resource ....

COHEN, P.R. and FEIGENBAUM, E.A. (Eds.) (1982): The Handbook of Artificial Intelligence, Vol. 3, Chapter XIII: Vision, William Kaufmann, Los Altos, California.

....salientcontour measure wewant should tell us the likelihood of an image contour at a given image position and orientation, based on the responses of oriented filters. It should favor long, straight curves, and continue over gaps. Anumber of approaches could be used, including relaxation labelling [24, 52, 44, 83], the approach of Grossberg and Mingolla [39, 40] snakes [53] or dynamic programming [95] Splines [11] or elastica [81, 80] could be used to interpolate across gaps. We implemented the dynamic programming method of Shaashua and Ullman [95] It solves an explicit 87 optimization problem, gives ....

P.R.CohenandE.A.Feigenbaum, editors. The Handbook of Artificial Intelligence, pages 292--300. Addison Wesley,1982.

....of using junction information to interpret scenes. 5.1 Related Work 5.1.1 Blocks World Vision researchers studying the blocks world developed important methods for using local information to interpret scene structure. The blocks world restricts scene objects to be polyhedral blocks. See [23] for a review) Guzman [42] made use of vertices and junctions to recognize 3 dimensional objects. He developed heuristics for grouping the elements of a line drawing into objects. Huffman [51] and Clowes [22] systematically labelled each line as corresponding to either a concave edge, a convex ....

P.R.CohenandE.A.Feigenbaum, editors. The Handbook of Artificial Intelligence, pages 139--194. Addison Wesley,1982. 125

....language processing, symbolic programming languages, expert systems, planning, vision, and deduction systems were all widely pursued but with a distinctly symbolic flavor and to the exclusion of connectionist techniques. This is punctuated by the fact that Cohen and Feigenbaum s classic textbook [10], largely considered the bible of the field, devotes a mere five pages out of three volumes to perceptron like learning techniques. Rumelhart and McClelland [53] are largely responsible for the renewed popularity of multilayer perceptrons that existed in the mid 1980 s. While they were not the ....

P.R. Cohen and E.A. Feigenbaum, editors. The Handbook of Artificial Intelligence, volume 3. AddisonWesley Publishing Company, Inc., 1982.

....The text is often several factors shorter than a complete object composition description, yet it contains the same information. Class dictionary graphs are an abstraction of semantic networks with part of and is a links. Semantic networks have been studied extensively in the AI literature [7]. Class dictionary graphs are also related to AND OR graphs which have been used in AI [7] The Interface Description Language described in [33] uses structures related to class dictionary graphs. However, the specific axiomatic structure which we introduce here as well as our efficient axiom ....

....yet it contains the same information. Class dictionary graphs are an abstraction of semantic networks with part of and is a links. Semantic networks have been studied extensively in the AI literature [7] Class dictionary graphs are also related to AND OR graphs which have been used in AI [7]. The Interface Description Language described in [33] uses structures related to class dictionary graphs. However, the specific axiomatic structure which we introduce here as well as our efficient axiom checking algorithm are new, to the best of our knowledge. The relationships between this ....

Paul R. Cohen and Edward A. Feigenbaum. The Handbook of Artificial Intelligence, volume 3. William Kaufmann, Inc., 1982.

....literal descriptions of simple movements, compared with the complex descriptions and interpretations produced by humans. 2.3. 1 Levels of Processing in Computer Vision Systems Computer vision has been defined as the information processing task of understanding a scene from its projected images (Cohen and Feigenbaum 1982:127) Computer vision systems take low level codings of still and moving images as input and produce higher level, symbolic classifications and descriptions of the objects and actions depicted by the pixels. Thus, research in computer vision has been characterised as following a signals to ....

Cohen and Feigenbaum (1982). Paul R. Cohen and Edward A. Feigenbaum, The Handbook of Artificial Intelligence. Reading MA: Addison-Wesley.

.... evolution, it was still required to endow the teacher role with some knowledge of the control actions needed (e.g. see [Widrow, et al., 1973] Continuing up into the early 1980 s, AI researchers who utilized critics were also typically obliged to provide the critic with domain specific knowledge [Cohen Feigenbaum, 1982]. In 1983, however, a very important extension was made in which the critic also learns, in this case, to provide increasingly better evaluation feedback; this removed the requirement for providing the teacher a priori knowledge about desired controller actions [Barto, et al., 1983] Historically, ....

Cohen, P.R. & E.A. Feigenbaum, 1982, The Handbook of Artificial Intelligence, vol 3, Kauffman.

.... there are cases in which on line learning is strictly harder than pac learning [B] 3 The Credit Assignment Problems The credit assignment problem may be defined as the problem of assigning credit or blame to the individual decisions that led to some overall results (Cohen and Feigenbaum [CF]) Obviously, this problem is ubiquitous not only in Artificial Intelligence, but also in the study of adaptive neural networks, where credit or blame for the overall performance of the network has to be distributed to the individual components of the network. In the study of learning unions of ....

P. Cohen, E. Feigenbaum, "The Handbook of Artificial Intelligence", Vol. 3, William Kaufman, 1982. 34

....a machine learning approach, using manually structured example portions to generate recognition grammars for automatically structuring the rest of the document. In this paper we present MarkItUp , a system which uses techniques for editing by example [12,13] and more generally learning by example [14,15] to gradually acquire recognition grammars. MarkItUp supports a structure editor which can be used to transform example portions of documents into SGML documents by inserting markups. Predefined recognition patterns, such as delimiters or application specific formats, are used to abstract from ....

P.R. Cohen and E.A. Feigenbaum, The handbook of artificial intelligence, volume 3, William Kaufmann, Los Altos, CA, 1982.

....and across displays, and unity addresses how to unify individual components into a coherent whole [14] In our approach, the design of a visual discourse is a sequence of visual actions. A visual action is an encoded graphic design technique (e.g. move an object) We use a hierarchical planner [4] to generate the sequence of actions. Compared to search based approaches, hierarchical planning achieves computational efficiency by reducing the amount of search needed [24] It also eases the task of knowledge encoding [20] by reusing the actions that are common to many design tasks. In ....

P. Cohen and E. Feigenbaum, editors. The Handbook of Artificial Intelligence, volume 3, chapter XV. Planning and Problem Solving. Addison -Wesley, Reading, MA, 1982.

....in mind, a typical fit and splitassignment problem can be characterized by describing a problem generator. The generator is listed in figure 4. 2. 4 Related Learning Problems The fit and split assignment problem is a specialization of the overlapping concept learning problem [Michalski, 1983; Cohen and Feigenbaum, 1982] where a class corresponds to a partial hypothesis. It differs from standard overlapping concept learning in two ways. First, examples are labeled with every class (that is, partial hypothesis) that covers them. In the standard problem, examples are labeled with only one covering class at a ....

P. Cohen and E. Feigenbaum. The Handbook of Artificial Intelligence. Volume 3, HeurisTech Press, Stanford, CA, 1982.

....the polymorphic method as an abstract concept. We are actively pursuing the formalization of these ideas at the present time. 4 Related work The fast algorithm which we present here has not been reported in the literature. Our work is a continuation of earlier work on inductive inference [CF82, Chapter XIV: Learning and inductive inference] AS83] Our contribution is an efficient algorithm for inductive inference 42 of high level class descriptions from examples. Related work has been done in the area of learning context free grammars from examples and syntax trees [AS83] The key ....

.... reported in [DJ88] In the relational database field various algorithms for deriving schemas in normal form have been developed to help the application builder to pin point design flaws [Lie85] The problem of learning classes from object examples has been studied earlier in AI (e.g. SM86] CF82] Clustering algorithms have been applied to build a tree of mutually exclusive classes from a given object set. Our work extends this earlier work since we have more structure in our classes, e.g. the capability to define a language. The axiomatic Demeter kernel model which is used in this ....

Paul R. Cohen and Edward A. Feigenbaum. The Handbook of Artificial Intelligence, volume 3. William Kaufmann, Inc., 1982.

....choices for action. At any point in time, they can choose to act on the basis of the constraints they have recalled and examined thus far, or they can extend 78 Thanks to Vincent Wright for introducing me to the term waffling. 79 e.g. PLANNER, CONNIVER, HACKER, etc. Barr and Feigenbaum, 1981; Cohen and Feigenbaum, 1982]. Chapter 5: A Constraint Directed Approach to Improvisation 169deliberation, allowing more information to accumulate (and potentially, opportunities to act to pass by) Choosing to act includes not only commitment to physical actions, but also to cognitive actions, such as adopting or ....

Cohen, Paul R., and Edward A. Feigenbaum (Eds.), The Handbook of Artificial Intelligence, Volume III (Los Altos, CA: William Kaufmann), 1982. 639 pp.

....circle or small square . In this case, there are multiple solution paths through the learning tree. In this case, it would be beneficial to be able to find the optimal path to any solution; this is a major goal of ID3. 3.1. 2 CLS Implementation This section outlines the CLS algorithm as given in [3]. Like most algorithms in any science, CLS has differing implementations depending on the final user s requirements; this document presents a general case suited to the examples presented. Define I to be a set of training instances. This set can be a random sampling of the full data set, or it ....

....to Figure 3, the original set of colored shapes is I ; after one iteration, the subsets are divided into two windows, based on their similarities to the feature F, shape . Each window is then divided into smaller windows based on the second level F, size . The ID3 algorithm abstract as given in [3] follows. 1. Randomly select a window from I . 1 2. Use CLS to form a rule regarding the structure of the decision tree produced by the window. 3. Iterate through the entire set I to find exceptions to the rule produced. 4. Form a new window by combining the current window with the ....

Cohen, Paul R, et. al. The Handbook of Artificial Intelligence, Volume 3. pages 406--411. HeurisTech Press, Stanford, CA, 1982.

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Cohen, P., Feigenbaum, E., Eds.: The Handbook of Artificial Intelligence, vol. 3, Morgan Kaufmann, 1981.

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Cohen, P. R. and Feigenbaum, E. A. 1982. The Handbook of Artificial Intelligence, volume 3. Kaufmann.

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P.R. Cohen and E.A. Feigenbaum, editors. The Handbook of Artificial Intelligence, volume 3. Morgan Kaufmann, 1981.

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Bar A. and Feigenbaum E. A. (1981) The Handbook of Artificial Intelligence, Kaufman.

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Paul R. Cohen and Edward A. Feigenbaum. The Handbook of Artificial Intelligence, volume 3. William Kaufmann, Inc., 1982.

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Cohen, P. and Feigenbaum, E.A., The Handbook of Artificial Intelligence 3 (Kaufmann, Los Altos, CA, 1982).

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Paul R. Cohen Avon Barr and Edward A. Feigenbaum. The Handbook of Artificial Intelligence, volume 4. Addison Wesley, Reading, MA, 1989.

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Baar, A., Cohen, P.R,. Feigenbaum, E.A.: The handbook of artificial intelligence 4 Addison Wesley (1989)

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P. R. Cohen and E. A. Feigenbaum. Handbook of Artificial Intelligence, Chap. XIII. William Kaufman, Los Altos, CA, 1982.

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