20,352 research outputs found
Robustness, stability, recoverability, and reliability in constraint satisfaction problems
The final publication is available at Springer via http://dx.doi.org/10.1007/s10115-014-0778-3Many real-world problems in Artificial Intelligence (AI) as well as in other areas of
computer science and engineering can be efficiently modeled and solved using constraint programming
techniques. In many real-world scenarios the problem is partially known, imprecise
and dynamic such that some effects of actions are undesired and/or several un-foreseen incidences
or changes can occur. Whereas expressivity, efficiency and optimality have been the typical
goals in the area, there are several issues regarding robustness that have a clear relevance in
dynamic Constraint Satisfaction Problems (CSP). However, there is still no clear and common
definition of robustness-related concepts in CSPs. In this paper, we propose two clearly differentiated
definitions for robustness and stability in CSP solutions. We also introduce the concepts
of recoverability and reliability, which arise in temporal CSPs. All these definitions are based on
related well-known concepts, which are addressed in engineering and other related areas.This work has been partially supported by the research project TIN2013-46511-C2-1 (MINECO, Spain). We would also thank the reviewers for their efforts and helpful comments.Barber SanchĂs, F.; Salido Gregorio, MA. (2015). Robustness, stability, recoverability, and reliability in constraint satisfaction problems. Knowledge and Information Systems. 44(3):719-734. https://doi.org/10.1007/s10115-014-0778-3S719734443Abril M, Barber F, Ingolotti L, Salido MA, Tormos P, Lova A (2008) An assessment of railway capacity. Transp Res Part E 44(5):774â806Barber F (2000) Reasoning on intervals and point-based disjunctive metric constraints in temporal contexts. J Artif Intell Res 12:35â86Bartak R, Salido MA (2011) Constraint satisfaction for planning and scheduling problems. Constraints 16(3):223â227Bertsimas D, Sim M (2004) The price of robustness. Oper Res 52(1):35â53Climent L, Wallace R, Salido M, Barber F (2013) Modeling robustness in CSPS as weighted CSPS. In: Integration of AI and OR techniques in constraint programming for combinatorial optimization problems CPAIOR 2013, pp 44â60Climent L, Wallace R, Salido M, Barber F (2014) Robustness and stability in constraint programming under dynamism and uncertainty. J Artif Intell Res 49(1):49â78Dechter R (1991) Temporal constraint network. Artif Intell 49:61â295Hazewinkel M (2002) Encyclopaedia of mathematics. Springer, New YorkHebrard E (2007) Robust solutions for constraint satisfaction and optimisation under uncertainty. PhD thesis, University of New South WalesHebrard E, Hnich B, Walsh T (2004) Super solutions in constraint programming. In: Integration of AI and OR techniques in constraint programming for combinatorial optimization problems (CPAIOR-04), pp 157â172Jen E (2003) Stable or robust? Whatâs the difference? Complexity 8(3):12â18Kitano H (2007) Towards a theory of biological robustness. Mol Syst Biol 3(137)Liebchen C, Lbbecke M, Mhring R, Stiller S (2009) The concept of recoverable robustness, linear programming recovery, and railway applications. In: LNCS, vol 5868Papapetrou P, Kollios G, Sclaroff S, Gunopulos D (2009) Mining frequent arrangements of temporal intervals. Knowl Inf Syst 21:133â171Rizk A, Batt G, Fages F, Solima S (2009) A general computational method for robustness analysis with applications to synthetic gene networks. Bioinformatics 25(12):168â179Rossi F, van Beek P, Walsh T (2006) Handbook of constraint programming. Elsevier, New YorkRoy B (2010) Robustness in operational research and decision aiding: a multi-faceted issue. Eur J Oper Res 200:629â638Szathmary E (2006) A robust approach. Nature 439:19â20Verfaillie G, Schiex T (1994) Solution reuse in dynamic constraint satisfaction problems. In: Proceedings of the 12th national conference on artificial intelligence (AAAI-94), pp 307â312Wallace R, Grimes D, Freuder E (2009) Solving dynamic constraint satisfaction problems by identifying stable features. In: Proceedings of international joint conferences on artificial intelligence (IJCAI-09), pp 621â627Wang D, Tse Q, Zhou Y (2011) A decentralized search engine for dynamic web communities. Knowl Inf Syst 26(1):105â125Wiggins S (1990) Introduction to applied nonlinear dynamical systems and chaos. Springer, New YorkZhou Y, Croft W (2008) Measuring ranked list robustness for query performance prediction. Knowl Inf Syst 16:155â17
Robustness, Stability, Recoverability and Reliability in Dynamic Constraint Satisfaction Problems
Many real-world problems in Artificial Intelligence (AI) as well as in other areas of computer science and engineering can be efficiently modeled and solved using constraint programming techniques. In many real-world scenarios the problem is partially known, imprecise and dynamic, so that some effects of actions are undesired and/or several un-foreseen incidences or changes can occur.
Whereas expressivity, efficiency, and optimality have been the typical goals in the area, several is-sues regarding robustness appear with a clear relevance in dynamic constraint satisfaction problems (DCSPs). However, there is still no a clear and common definition of robustness-related concepts in CSPs. In this paper, we propose two clearly differentiated definitions for robustness and stability in CSP solutions. We also introduce the concepts of recoverability and reliability which arise in temporal DCSPs. All these definitions are based on related well-known concepts addressed in engineering and other related areas.Barber SanchĂs, F.; Salido Gregorio, MA. (2011). Robustness, Stability, Recoverability and Reliability in Dynamic Constraint Satisfaction Problems. http://hdl.handle.net/10251/1070
Strategic maritime container transport design in oligopolistic markets
AbstractThis paper considers the maritime container assignment problem in a market setting with two competing firms. Given a series of known, exogenous demands for service between pairs of ports, each company is free to design a liner service network serving a subset of the ports and demand, subject to the size of their fleets and the potential for profit. The model is designed as a three-stage complete information game: in the first stage, the firms simultaneously invest in their fleet; in the second stage, they individually design their networks and solve the route assignment problem with respect to the transport demand they expect to serve, given the fleet determined in the first stage; in the final stage, the firms compete in terms of freight rates on each origin-destination movement. The game is solved by backward induction. Numerical solutions are provided to characterize the equilibria of the game
Applications of negotiation theory to water issues
The authors review the applications of noncooperative bargaining theory to waterrelated issues-which fall in the category of formal models of negotiation. They aim to identify the conditions under which agreements are likely to emerge and their characteristics, to support policymakers in devising the"rules of the game"that could help obtain a desired result. Despite the fact that allocation of natural resources, especially trans-boundary allocation, has all the characteristics of a negotiation problem, there are not many applications of formal negotiation theory to the issue. Therefore, the authors first discuss the noncooperative bargaining models applied to water allocation problems found in the literature. Key findings include the important role noncooperative negotiations can play in cases where binding agreements cannot be signed; the value added of politically and socially acceptable compromises; and the need for a negotiated model that considers incomplete information over the negotiated resource.Water Supply and Sanitation Governance and Institutions,Town Water Supply and Sanitation,Water and Industry,Environmental Economics&Policies,Water Conservation
The GRT Planning System: Backward Heuristic Construction in Forward State-Space Planning
This paper presents GRT, a domain-independent heuristic planning system for
STRIPS worlds. GRT solves problems in two phases. In the pre-processing phase,
it estimates the distance between each fact and the goals of the problem, in a
backward direction. Then, in the search phase, these estimates are used in
order to further estimate the distance between each intermediate state and the
goals, guiding so the search process in a forward direction and on a best-first
basis. The paper presents the benefits from the adoption of opposite directions
between the preprocessing and the search phases, discusses some difficulties
that arise in the pre-processing phase and introduces techniques to cope with
them. Moreover, it presents several methods of improving the efficiency of the
heuristic, by enriching the representation and by reducing the size of the
problem. Finally, a method of overcoming local optimal states, based on domain
axioms, is proposed. According to it, difficult problems are decomposed into
easier sub-problems that have to be solved sequentially. The performance
results from various domains, including those of the recent planning
competitions, show that GRT is among the fastest planners
Applications of negotiation theory to water issues
The purpose of the paper is to review the applications of non-cooperative bargaining theory to water related issues â which fall in the category of formal models of negotiation. The ultimate aim is that of, on the one hand, identify the conditions under which agreements are likely to emerge, and their characteristics; and, on the other hand, to support policy makers in devising the ârules of the gameâ that could help obtain a desired result. Despite the fact that allocation of natural resources, especially of trans-boundary nature, has all the characteristics of a negotiation problem, there are not many applications of formal negotiation theory to the issue. Therefore, this paper first discusses the noncooperative bargaining models applied to water allocation problems found in the literature. Particular attention will be given to those directly modelling the process of negotiation, although some attempts at finding strategies to maintain the efficient allocation solution will also be illustrated. In addition, this paper will focus on Negotiation Support Systems (NSS), developed to support the process of negotiation. This field of research is still relatively new, however, and NSS have not yet found much use in real life negotiation. The paper will conclude by highlighting the key remaining gaps in the literature.Negotiation theory, Bragaining, Coalitions, Fairness, Agreements
The CIFF Proof Procedure for Abductive Logic Programming with Constraints: Theory, Implementation and Experiments
We present the CIFF proof procedure for abductive logic programming with
constraints, and we prove its correctness. CIFF is an extension of the IFF
proof procedure for abductive logic programming, relaxing the original
restrictions over variable quantification (allowedness conditions) and
incorporating a constraint solver to deal with numerical constraints as in
constraint logic programming. Finally, we describe the CIFF system, comparing
it with state of the art abductive systems and answer set solvers and showing
how to use it to program some applications. (To appear in Theory and Practice
of Logic Programming - TPLP)
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