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In Sections 33 and 34 we have brie y described some similarity measures and then introduced the concept of fuzzy similarity, which are closely based on the feature weights Essentially, feature weights describe the signi cance, and hence the relation, of various features This means that if a feature is more relevant in terms of characterizing cases, it should have more weight (importance) Thus, case feature weighting is crucial in computing similarity, especially weighted distance and weighted feature similarity, which have been introduced in Section 34 The problem of weighting features (ie, assigning weights to features) in a soft computing framework has been investigated by many researchers; see, for example, the recent unsupervised feature selection [47,48] and feature extraction [49] algorithms using a neuro-fuzzy approach [23] and a GA-based approach [50] In this section, some of them are presented.

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[167] D Gale, The Theory of Linear Economic Models, Nueva York, EEUU: McGraw-Hill Book Company, Inc, 1960 [168] HW Kuhn, The hungarian method for the assignment problem , Naval Research Logistics Quarterly, vol 2(1), pp 83 97, 1955 [169] DP Bertsekas, The auction algorithm for assignment and other network ow problems: a tutorial , Interfaces, vol 20(4), pp 133 149, 1990 [170] P Brucker, Scheduling Algorithms, Berlin: Springer-Verlag, 1998 [171] JL Bruno, EG Coffmann and R Sethi, Scheduling independent tasks to reduce mean nishing time , Communications of the ACM, vol 17(7), pp 382 387, 1974 [172] MB Dias and A Stentz, A market approach to multi-robot coordination , The Robotics Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, Tech Rep CMU-RI-TR01-26, 2001 [173] KL Moore, Y Chen and Z.

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Let us consider a CBR system where a case is usually represented by a feature vector with n components Then the global feature weight refers to a vector w1 ; w2 ; ; wn , where each of its components is a real number in [0,1] It can be interpreted in the way that for the entire case library, different features have different degrees of importance to the solution This is different from the concept of a local feature weight A global feature weight is assigned to each feature of a case, and the same feature for all the cases has the same global weight On the other hand, a local feature weight is assigned to each feature of a speci c case, and a feature may have different local weights for different cases.

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which aim at protecting the environment and preserving natural resources (energy, matter, raw materials). Green technologies are to be found in all sectors of the economy. They also include antipollution technologies that are used to tackle any air, water and soil pollution. An important application area for green technologies is waste treatment, in order to avoid polluting the environment and to recycle the maximum amount of raw materials which are used. Wastes, which represent a risk for the environment, can therefore become a resource:

Song, Diffusion-based path planning in mobile actuatorsensor networks (mas-net): some preliminary results , in Proceedings of SPIE, April 2004 [174] RS Aylett and DP Barnes, A multi-robot architecture for planetary rovers , in Proceedings of the 5th ESA Workshop on Space Robotics, ASTRA 98, Noordwijk: European Space Agency, 1998 [175] M Mataric, M Nilsson and KT Simsarian, Cooperative multi-robot box-pushing , in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, vol 3 Los Alamitos, CA: IEEE Computer Society Press, 1995, pp 556 561 [176] D Rus, B Donald and J Jennings, Moving furniture with teams of autonomous robots , in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, vol 1, Pittsburgh, PA: IEEE, 1995, pp 235 242 [177] LE Parker, Alliance: An architecture for fault tolerant multi-robot cooperation , IEEE Transactions on Robotics and Automation, vol 14(2), pp.

In this section, a gradient-based optimization technique and neural networks are used to learn the global feature weights Gradient-based optimization techniques are useful in determining the search directions based on the derivative information of an objective function The optimization is carried out by minimizing a real-valued objective function de ned on an n-dimensional input space In learning the feature weights, an individual weight.

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220 240, 1998 [178] YU Cao, AS Fukunaga and A Kahng, Cooperative mobile robotics: antecedents and directions , Autonomous Robots, vol 4(1), pp 7 27, 1997 [179] G Dudek, M Jenkin and E Milios, Robot Teams: From Diversity to Polymorphism, Natick, MA, EEUU: AK Peters, 2002, pp 3 22 [180] S Yuta and S Premvuti, Coordinating autonomous and centralized decision making to achieve cooperative behaviors between multiple mobile robots , in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Raleigh, North Carolina, 1992, pp 1566 1574 [181] T Balch, Taxonomies of multi-robot task and reward , Carnegie Mellon University, Pittsburgh, Pennsylvania, Tech Rep, 1998 [182] K Ali, Multiagent telerobotics: matching systems to tasks , PhD dissertation, Georgia Institute of Technology, 1999 [183] E Todt, G Raush and R.

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