Haralick, R. & G. Elliot (1980). Increasing tree search efficiency for constraint satisfaction problems. Artificial Intelligence, 14:263--313.  Home/Search Document Not in Database Summary Related Articles Check |
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Satellite Tele-Communications Scheduling As Dynamic.. - Plaunt, Jonsson, Frank (Correct)
....Adding such a preference order among solutions satisfying the constraints results in a Constrained Optimization Problem or COP. CSPs and COPs have been heavily studied, and many theoretical and practical results can be brought to bear to address such problems; for work on CSPs in general see Haralick Elliot (1980), Nadel (1989) and for a specific application see Banerjee Frank (1996) However, as calls arrive and depart, we have not just one but a sequence of such problems. These problems are closely related, as each problem in the sequence is derived from an earlier problem by the termination of an ....
Haralick, R. & G. Elliot (1980). Increasing tree search efficiency for constraint satisfaction problems. Artificial Intelligence, 14:263--313.
Artificial Intelligence Search Algorithms - Korf (1998) (8 citations) (Correct)
....if all solutions are to be found. If only a single solution is required, however, value ordering can decrease the time required to find a solution. In general, one should order the values from least constraining to most constraining, in order to minimize the time required to find a first solution[5, 12]. An important idea, originally called backjumping, is that when an impass is reached, instead of simply undoing the last decision made, the decision that actually caused the failure should be modified[11] For example, consider a three variable problem where the variables are instantiated in the ....
Haralick, R.M., and G.L. Elliott, Increasing tree search efficiency for constraint satisfaction problems, Artificial Intelligence, Vol. 14, 1980, pp. 263-313.
A Graph Based Synthesis Algorithm for Solving CSPs - Pang, Goodwin (2001) (Correct)
....combinations of values. Since many problems in AI and other areas of computer science can be formulated as CSPs, it has been a research subject for a long time and researchers have approached the subject in different directions: searching for the CSP s solutions from the possible solution space ([10, 7, 3]) reducing a CSP to a simpler and equivalent CSP ( 13, 14, 15, 9, 1] and synthesizing solutions from partial solutions ( 6, 21, 22, 17] Solution synthesis finds all solutions for a given CSP. It can also be used to find all partial solutions with respect to a particular subset of variables. ....
R. Haralick and G. Elliott. Increasing tree search efficiency for constraint satisfaction problems. Artificial Intelligence, 14:263--313, 1980.
Intelligent Backtracking in Logic Programming with Constraints .. - Kotzamanidis (1995) (Correct)
.... Language CHIP Constraint programming in procedural languages has been studied in the area of Artificial Intelligence for many years [SJ80, Dav87] Constraint satisfaction problems (CSP) where variables take values over finite domains have also attracted much attention in the AI community [Mac77, HE80, Dec90, Nad87]. Research in this area was stimulated by the well known thrashing behaviour of naive backtracking. An excellent discussion of this behaviour can be found in [Mac77] A thorough discussion of CSP and its various algorithms can be found in [Tsa92] The aim of this work was to apply the constraints ....
....terms of memory management) implementations of the language (see [War77, AK92] it suffers from the well known thrashing behaviour of the naive backtracking mechanism. These inefficiencies have been identified and studied by researchers in the area of Logic Programming and Artificial Intelligence [Gas77, Mac77, HE80, Nad87, Dec90]. Intelligent backtracking methods identify the causes of a particular unification failure and try not to explore branches of the search space that would again lead to the same failure. All intelligent backtracking schemes that have been proposed in the literature are based on different methods ....
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R.M. Haralick and G.L. Elliot. Increasing Tree Search Efficiency for Constraint Satisfaction Problems. Artificial Intelligence, 14:263--313, 1980.
A Contracting Model for Flexible Distributed Scheduling - Sen, Durfee (1996) (5 citations) (Correct)
....of Search Bias How should we bias the distributed search process to yield desirable solutions We view a search bias as an a priori measure of the goodness of certain particular solutions being evaluated. In this respect, different search biases can be related to different Value Goodness measures [11], as used in the constraint satisfaction literature. We now present three different search biases, identifying how they work, and the types of solutions (calendar profiles) they generate: Linear early (LE) This search bias was used in our earlier work to produce calendars with a density profile ....
R.M. Haralick and G.L. Elliott. Increasing tree search efficiency for constraint satisfaction problems. Artificial Intelligence, 14(3):263--313, 1980.
Réseaux De Contraintes - Schiex (2000) (Correct)
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Haralick, R. M., et Elliot, G. L. 1980. Increasing tree search efficiency for constraint satisfaction problems. Artificial Intelligence 14 :263--313. 5, 8
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