Download PDF by Ian Miguel BSC, MSc, PhD (auth.): Dynamic Flexible Constraint Satisfaction and its Application

By Ian Miguel BSC, MSc, PhD (auth.)

ISBN-10: 0857293788

ISBN-13: 9780857293787

ISBN-10: 144711048X

ISBN-13: 9781447110484

First, i want to thank my primary manager Dr Qiang Shen for all his aid, recommendation and friendship all through. Many thank you additionally to my moment manager Dr Peter Jarvis for his enthusiasm, aid and friendship. i'd additionally prefer to thank the opposite participants of the Approximate and Qualitative Reasoning crew at Edinburgh who've additionally helped and encouraged me. This undertaking has been funded by means of an EPSRC studentship, award num­ ber 97305803. i need, accordingly, to increase my gratitude to EPSRC for helping this paintings. Many because of the workers at Edinburgh collage for all their aid and aid and for swiftly solving any technical difficulties that i've got had . My entire kin were either encouraging and supportive through the finishing touch of this e-book, for which i'm perpetually indebted. York, April 2003 Ian Miguel Contents checklist of Figures XV 1 creation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1. 1 fixing Classical CSPs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1. 2 Applicat ions of Classical CSP . . . . . . . . . . . . . . . . . . . . . . . . . . . . three 1. three barriers of Classical CSP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1. three. 1 versatile CSP 6 1. three. 2 Dynamic CSP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1. four Dynamic versatile CSP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1. five versatile making plans: a DFCSP program . . . . . . . . . . . . . . . . . . eight 1. 6 constitution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . nine 1. 7 Contributions and their importance eleven 2 The Constraint pride challenge thirteen 2. 1 Constraints and Constraint Graphs . . . . . . . . . . . . . . . . . . . . . . . thirteen 2. 2 Tree seek resolution options for Classical CSP . . . . . . . . . . sixteen 2. 2. 1 go into reverse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2. 2. 2 Backjumping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2. 2. three Conflict-Directed Backjumping . . . . . . . . . . . . . . . . . . . . . 19 2. 2. four Backmarking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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This technique is based on the following observation: if it is true that the assignment of a domain element di ; to Xi is consistent, then the sub-problem formed by restricting D, to di ; can be made arc consistent. Any domain element for which this property does not hold can be filtered. 3 Pre-Processing Techniques 31 of filtering achieved. Also, neighbourhood inverse consistency was found to be very powerful in terms of filtering domain elements, but also very timeconsuming. 6 Directional Pre-Processing To improve the efficiency of pre-processing a esp, Dechter and Pearl [35] proposed directional pre-processing algorithms that exploit the order of instantiation that a tree search algorithm will subsequently use.

Any domain element for which this property does not hold can be filtered. 3 Pre-Processing Techniques 31 of filtering achieved. Also, neighbourhood inverse consistency was found to be very powerful in terms of filtering domain elements, but also very timeconsuming. 6 Directional Pre-Processing To improve the efficiency of pre-processing a esp, Dechter and Pearl [35] proposed directional pre-processing algorithms that exploit the order of instantiation that a tree search algorithm will subsequently use.

1 Constraints and Constraint Graphs Xl, D, 15 = {P,K,B} < xs,D s = {V,U,N} x4,D4 = {K,J,E,B} Fig. 1. Example CSP: Constraint Graph Representation. 2. Using the dual graph representation, arbitrary k-ary CSP can be solved using techniques developed for binary constraint graphs [31]. An intermediate representation, known as the hidden variable encoding [156] contains both dual variable nodes and nodes representing individual variables. Constraints between each node representing a variable Xi and the dual nodes representing constraints involving Xi specify that the assignment to the variable must match the assignment to Xi in a tuple assigned to the dual variable.

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Dynamic Flexible Constraint Satisfaction and its Application to AI Planning by Ian Miguel BSC, MSc, PhD (auth.)


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