Barr A, Cohen P R and Feigenbaum E A, 1989 Handbook of Artificial Intelligence, AddisonWesley. Home/Search Document Not in Database Summary Related Articles Check |
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An Algorithmic Model for Real Time Computation - Akl (2002) (Correct)
....state is always reduced (it never stays the same or grows) provided that the algorithm used to reach S(i Gamma 1) is also used to reach S(i) In Fig. 3.1, S(i) is closer to FINISH than S(i Gamma 1) for i = 1; 2; m. Henceforth, we refer to this computation as State Space Traversal (SST) [9]. Note that SST describes a computational framework (that is, a setting within which a family of computations sharing a set of properties can be defined) rather than a specific computational problem. Variants of this computational framework are discussed in Section 7. It may be helpful in what ....
A. Barr and E.A. Feigenbaum, Eds., The Handbook of Artificial Intelligence, Vol. I, William Kaufmann, Los Altos, California, 1981.
Discrete Steepest Descent In Real Time - Akl (2001) (1 citation) (Correct)
....state is always reduced (it never stays the same or grows) provided that the algorithm used to reach S(i Gamma 1) is also used to reach S(i) In Fig. 1, S(i) is closer to FINISH than S(i Gamma 1) for i = 1; 2; m. Henceforth, we refer to this computation as State Space Traversal (SST) [9]. Note that SST describes a computational framework (that is, a setting within which a family of computations sharing a set of properties can be defined) rather than a specific computational problem. It may be helpful in what follows to think of the index i, where 1 i m, as denoting the ith ....
A. Barr and E.A. Feigenbaum, Eds., The Handbook of Artificial Intelligence, Vol. I, William Kaufmann, Los Altos, California, 1981.
A Control System Architecture For Intelligent Machine Systems - Albus (Correct)
.... decomposition, hierarchical real time planning, model driven image analysis [10 14] blackboards [14] and expert systems into a systems framework with modern control concepts such as multivarient state space control, reference model adaptive control, dynamic optimization, and learning systems [1518 ] The MAUV architecture framework also readily accommodates concepts from operations research, differential games, utility theory, and value driven reasoning [1, 19 20] A block diagram of the NBS MAUV control system architecture is shown in Figure 4. In the MAUV control system architecture ....
A. Barr, E. Feigenbaum, The Handbook of Artificial Intelligence, (Los Altos, William Kaufman, 1981).
J.C. Baltzer AG, Science Publishers - Neural Network Models (Correct)
....During the past decade, expert systems have become the first practical application of artificial intelligence. However, many expert system applications are rule based systems, which require a time consuming and difficult knowledge acquisition process to extract expertise from domain experts [1 3]. Neurocomputing is the application of artificial neural networks to practical problems. An artificial neural network consists of processing elements (which is analogous to neurons in the biological neural system) in an interconnected network [4 11] Aprocessing element accepts inputs, ....
A. Barr and E.A. Feigenbaum, The Handbook of Artificial Intelligence, Vol. 2, Pittman, Boston, MA, 1983.
An Evolutionary Knowledge-Based Framework for Reverse.. - Stefan Trausan-Matu.. (Correct)
....filled with other object s) for control flow, for data flow, for object decomposition, and for abstraction and refinement to other objects. Production rules are the most popular knowledge representation technique. They are very suitable to represent heuristic knowledge as situation action pairs [8,10]. XRL rules are implemented as objects. This implies that rules may be related in inheritance hierarchies similar to concept taxonomies. For example, the set of rules that detects which are the usages of an array in a program has the following structure: RULE STORE SORT STACK QUEUE ACCUM ....
.... 1) group map t2 :to t2 :of (res n) do ( i 1 (1 n) i n) group connect t2 :of (res i) to t1 :of (res (1 i) connect r :of (res i) to (term i) of sum) connect r :of (res n) to (term n) of sum) The concurrent refinement module [6] offers a blackboard based [8] problemsolving architecture for instantiating (considered as refinement) XRL objects. Associated with this architecture, a refinement language is defined. For example, the generic object description below is a declarative specification of a procedure for the addition of the positive elements from ....
A. Barr, E.A. Feigenbaum,.(eds), "The Handbook of Artificial Intelligence", vol.2, Morgan Kaufman, 1982.
What insights can be obtained from Temporal Physiological.. - Derek Sleeman Zhi (Correct)
....P2(1) is that any occurrence of a P1 at level 3 followed by a P2 at level 1, will cause the event, and that the lengths of these events are not significant. This pattern generating process is similar to the one used in the Chemical structural generator of Stanford s DENDRAL project in the 70s, [4]. Experiment C: The algorithm was used to generate patterns for several patient events, and we asked several clinicians to eliminate patterns (patterns of physiological events) which they thought would be impossible or very unlikely. If we are able to eliminate a sizable number of patterns then ....
A. Barr, & E.A. Feigenbaum (1981) The Handbook of Artificial Intelligence, Vol 1. Menlo Park, CA: William Kaufmann, pp 106-123.
MANTRA: A Multi-Level Hybrid Knowledge Representation.. - Bittencourt, Calmet.. (1994) (Correct)
.... Generality Rule Order 6 6 6 Context filter Uniqueness Context Order Recency Generality Rule Order 6 6 6 6 Rule filter Rule Order Uniqueness Recency 6 6 Figure 2: Conflict Resolution Strategies The meaning of each strategy, e.g. recency or generality, is defined as usual [1]. Moreover, the user can explicitly determine the flow strategy which is either allrules (breadth first search) or first rule (depth first search) The lisp like syntax of the language at this level including the explanation facilities is the following: heuristic level : rbase declaration j ....
A. Barr, E.A. Feigenbaum, The Handbook of Artificial Intelligence, William Kaufmann, 1982.
Hierarchical Learning with Procedural Abstraction Mechanisms - Rosca (1997) (21 citations) (Correct)
....simple solutions is by including a complexity penalty in the evaluation function. 2. 1 Why procedural representations A representation of knowledge is a combination of data structures and interpretive procedures that, if used in the right way in a program, will lead to knowledgeable behavior [Barr and Feigenbaum, 1981]. Two particular types of representation have played an important role in the development of AI ideas: declarative and procedural representations. A natural question is why use procedural representations when the field of symbolic AI, at least, has apparently moved from procedural representations ....
Avron Barr and Edward A. Feigenbaum, editors, The Handbook of Artificial Intelligence, volume I, HeurisTech Press, Stanford, California, 1981.
Query Languages for Geographic Information Systems - Egenhofer, Frank (Correct)
....within such a theory. The best known language for a formal model is first order logic which expresses facts and rules in a single, formalized matter [Gallaire 1984b] and derives knowledge by using formal rules. The deductive power of logic inference systems is typically used in AI systems (Barr 1982] [Hayes Roth 1983] Geographic Information Systems need these methods to help integrate data from different sources into a unified system [Robinson 1987a] A deficiency of any AI based system is the quantitative difference between AI expert systems and database management systems [Mylopoulos ....
A. Barr and E. Feigenbaum. The Handbook of Artificial Intelligence. Pitman, London,1982.
Context in Human-Machine Problem Solving: A Survey - Brézillon (1996) (Correct)
.... buttons which are functional, customizable and shareable [Maskery and Meads, 1992] possible worlds [Mylopoulos et al. 1990] assumptions under which a statement is true or false [Cavalcanti, 1993] a special, buffer like data structure, or an interpreter which controls the system s activity [Barr and Feigenbaum, 1981], the characteristics of the situation and the goals of the knowledge use [Bastien, 1992] entities (things or events) related in a certain way [Ogden and Richards, 1946] the possibility that permits to listen what is said and what is not said [Winograd and Flores, 1989] The first works ....
Barr A. and E.A. Feigenbaum, Eds. (1981) "The Handbook of Artificial Intelligence", William Kaufmann, Inc., Vol. 1, Chap III: Representation of Knowledge, reprsentation".
Interactive Recognition and Representation of Functionality - Bogoni, Bajcsy (Correct)
....presented by [2] addressed the relation between objects involved in an interaction. However, the representation of functionality must both consider the aspects defined but also capture the description of the interaction. 5 A review of these forms of knowledge representations can be found in [4]. 11 A functionality class refers not only to a group of objects but also to the actions employed in the application of the object, the feedback used to control and monitor the interaction as well as possible action to verify whether the functional interaction was successful. In the case of ....
A. Barr and E.A. Feigenbaum. Handbook of Artificial Intelligence, volume I. William Kaufmann, Los Altos CA, 1981.
A New Approach for Satellite Image Analysis By Means of a .. - Kunz, Schilling, Vögtle (1997) (2 citations) (Correct)
....general and specific knowledge about the topographic objects is used. Therefore, the analysis system must be able to represent and process this knowledge. Different formalisms for knowledge representation are known like predicate logic, rule based systems, formal grammatics or semantic networks (Barr and Feigenbaum 1982); Schefe 1986) Winston 1987) Niemann and Bunke 1987) Lusti 1990) 4.1 Semantic network Semantic networks belong to the most usable schemes concerning knowledge representation (Paulus 1992) The semantic networks so far have been used for speech recognition (Kummert 1992) industrial ....
Barr A., E. Feigenbaum (1982) The Handbook of Artificial Intelligence,Vol.2,Kauf- mann, Los Altos, CA.
Algorithms For Searching Massive Graphs - Agrawal, Jagadish (1994) (4 citations) (Correct)
....the number of data points explored by a search algorithm, such as Dijkstra s algorithm [13] for finding the shortest path between two points. 1. We are using the phrase open to represent the set of possible next moves as used in the AI literature on search algorithms (see [7], for example) If a node under consideration is opened, then we shall, perhaps at a later time, investigate the possibility of the desired shortest path going through that node. If the bounds indicate that the desired shortest path cannot pass through the node, it is not opened. 3 The issue ....
....simulation results that support our analysis developed in the previous sections regarding the effectiveness of our techniques. Some generalizations and related issues have been discussed in Section 6. We present our conclusions in Section 7. 2. DOMAIN ENCODING In the state space search paradigm [7], to find the solution of a problem, a search algorithm starts from one or more initial states and finds paths to the goal states. In the absence of any criteria for determining whether an intermediate node should be explored (opened) the number of nodes explored before arriving at a solution is ....
A. Barr and E. A. Feigenbaum, The Handbook of Artificial Intelligence, William Kaufman, Los Altos, California, 1981.
Genetic Logic Programming - Thomas Osborn Adib (1995) (7 citations) (Correct)
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Barr A, Cohen P R and Feigenbaum E A, 1989 Handbook of Artificial Intelligence, AddisonWesley.
On Sets of Terms: - Study Of Generalisation (Correct)
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A. Barr, E.A. Feigenbaum, "The Handbook of Artificial Intelligence". Vol.1, W. Kaufmann ed., California 1981.
Pattern-Based Languages for Prototyping of Compiler Optimizers - Charles Donald Farnum (1990) (3 citations) (Correct)
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Avron Barr & Edward A. Feigenbaum, eds., The Handbook of Artificial Intelligence, William Kaufmann, Inc., Los Altos, CA, 1982.
Contextual System of Symbol Structural Recognition.. - Delalandre.. (2005) (Correct)
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A. Barr, P. Cohen, and E. Feigenbaum. The Handbook of Artificial Intelligence, volume 1-4. Addison Wesley Publisher, 1989.
An Analysis of Diversity in Genetic Programming - Gustafson (2004) (Correct)
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Barr, A., Cohen, P., and Feigenbaum, E., editors (1989). The Handbook of Artificial Intelligence, volume 4. Addison-Wesley, Reading, MA.
The EXODUS Optimizer Generator - Goetz Graefe David (1987) (100 citations) (Correct)
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A. Barr and E.A. Feigenbaum, The Handbook of Artificial Intelligence, William Kaufman, Inc., Los Altos, CA. (1981). 25
A Highly Adaptable Concept For Visual Inspection - Sablatnig (1997) (Correct)
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A. Barr, E.A. Feigenbaum, "The Handbook of Artificial Intelligence", Vol.1, Vol.2, Pitmann Books, London, 1981.
Behavior Verification of Hybrid Real-time Requirements - Qualitative Formalism An (Correct)
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A. Barr, P. R. Cohen, and E. A. Feigenbaum, The Handbook of Artificial Intelligence, Vol. IV, Addison-Wesley Pub. 1989.
Lexical Analysis of Inflected Arabic Words using Exhaustive Search .. - Rafea (1993) (Correct)
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A. Barr and E. Feigenbaum, Handbook of Artificial Intelligence, Vol. 1, William Kaufman Inc., Los Altos, California, 1981.
Envisionment-Based Scheduling Using Time Interval Petri Networks: .. - Bulitko (1999) (Correct)
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A. Barr, P.R. Cohen, E.A. Feigenbaum (eds.). The Handbook of Artificial Intelligence. Volume IV, Chapter XVI by H.P.Nii. Addison-Wesley. 1989.
SEED Database Requirements - Snyder, Flemming, al. (1994) (Correct)
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Barr, A., & Feigenbaum, E. (Eds.). (1981). The Handbook of Artificial Intelligence (Vol. I). Los Altos: William Kaufmann, Inc. A collection of survey papers reporting on the initial results of Artificial Intelligence research. Includes both a comprehensive coverage of the topics as well as large author and bibliographic indexes.
Context Needs In Cooperative Building Of Explanations - Brézillon (1994) (1 citation) (Correct)
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Barr A. and E.A. Feigenbaum (eds.), "The Handbook of Artificial Intelligence", William Kaufmann, Inc., 1981, Vol. 1, Chap III: Representation of Knowledge.
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