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In the context of service discovery, matchmakers check the compliance of service-level objectives from providers and consumers. The problem of bounded uncertainty arises if some property is non-fixable. In this case, the provider is not able to control the value it takes at runtime, so the eventual consumer must not have the choice to select a value and fix it, but only knowing the guaranteed range of values it may take. To the best of our knowledge, there does not exist any approach which deals with this scenario. Most matchmakers work as if all properties were fixable, and a few have assumed the contrary. In either case, the accuracy of their results is likely to be in question since there may be involved both fixable and non-fixable properties at the same time, and there may also exist dependencies between them. In order to improve the accuracy, we present a holistic approach to matchmaking under bounded uncertainty and propose constraint programming as our choice to deal with it, so that matchmaking is transformed into a quantified constraint satisfaction problem. Comisión Interministerial de Ciencia y Tecnología TIN2006-00472 Comisión Interministerial de Ciencia y Tecnología TIN2009- 07366 Junta de Andalucía TIC-2533

Service Oriented Architecture (SOA) holds promise for business agility since it allows business process to change to meet new customer demands or market needs without causing a cascade effect of changes in the underlying IT systems. Business rules are the instrument chosen to help business and IT to collaborate. In this paper, we propose the utilization of simulation models to model and simulate strategic business rules that are then disaggregated at different levels of an SOA architecture. Our proposal is aimed to help find a good configuration for strategic business objectives and IT parameters. The paper includes a case study where a simulation model is built to help business decision-making in a context where finding a good configuration for different business parameters and performance is too complex to analyze by trial and error.

Web services bring programmers a new way to develop advanced applications able to integrate any group of services on the Internet into a single solution. Web services procurement (WSP) is focussed on the acquisition of web services, including some complex tasks such as the specification of demands, the search for available offers, and the best choice selection. Although the technology to support them already exists, there are only a few approaches wherein quality-of-service in demands and offers is taken into account, in addition to functionality. In this paper, we present some implementation issues on a quality-aware approach to WSP, whose solution is mainly based on using mathematical constraints to define quality-of-service in demands and offers.

As government agencies and business become more dependent on web services, software solutions to automate their procurement gain importance. Current approaches for automating the procurement of web services suffer from an important drawback: neither uncertainty measures nor non-linear, and complex relations among parameters can be used by providers to specify quality-of-service in offers. In this paper, we look deeply into the roots of this drawback and present a proposal which overcomes it. The key point to achieve this improvement has been using the constraint programming as a formal basis, since it endows the model with a very powerful expressiveness. A XML-based implementation is presented along with some experimental results and comparisons with other approaches.

This work presents the application of Constraint Programming to the problem of selecting and sequencing assembly operations. The set of all feasible assembly plans for a single product is represented using an And/Or graph. This representation embodies some of the constraints involved in the planning problem, such as precedence of tasks, and the constraints due to the completion of a correct assembly plan. The work is focused on the selection of tasks and their optimal ordering, taking into account their execution in a generic multi-robot system. In order to include all different constraints of the problem, the And/Or graph representation is extended, so that links between nodes corresponding to assembly tasks are added, taking into account the resource constraints. The resultant problem is mapped to a Constraint Satisfaction Problem (CSP), and is solved using Constraint Programming, a powerful programming paradigm that is increasingly used to model and solve many hard real-life problems.

Configuration tasks are an important application area in engineering design. The proposed solving techniques use either a constraintbased framework or a logic-based approach. We propose a methodology to obtains desired configuration using basic configuration cells(BCC). They are built by means of the predefined components and connections of the given configuration problem. In practical applications of configuration tasks the BCCs and configuration goals are represented according to object-oriented programming paradigm. They are mapped into a numeric constraint satisfaction problem. The transformation of a basic configuration cell into a new one generates a sequence of numeric constraint satisfaction problems. We propose an algorithm that solves this sequence of problems in order to obtain a configuration solution according to the desired requirements or that detects inconsistencies in the requirements. The integration of objectoriented and constraint programming paradigms allows us to achieve a synergy that produces results that could not be obtained if each one were working individually.

A method based on the union of an Evolutionary Algorithm (EA) and a local search algorithm for obtaining coordinated motion plans of two manipulator robots is presented. A Decoupled Planning Approach has been used. For this purpose, the problem has been decomposed into two subproblems: path planning, where a collision-free path is found for each robot independently of the other, only considering fixed obstacles; and trajectory planning, where the paths are timed and synchronized in order to avoid collision with the other robot. This paper focuses on the second problem. A method is presented to minimize the total motion time of two manipulators along their paths, avoiding collision regardless of the accuracy of the dynamic model used. A hybrid technique with EA and local search methods has been implemented.

This article describes a new system for learning rules using rotated hyperboxes as individuals of a genetic algorithm (GA). Our method attempts to find out hyperboxes at any orientation by combining deterministic hill-climbing with GA. Standard techniques, such as C4.5, use hyperboxes that are aligned with the coordinate axes. The system uses the decision queue (DQ) as method of representing the rule set. It means that the obtained rules must be applied in specific order, that is, an example will be classify by the i-rule only if it doesn’t satisfy the condition part of the i-1 previous rules. With this policy, the number of rules is less because the rules could be one inside of another one. We have tested our system on real data from UCI repository. Moreover, we have designed some two-dimensional artificial databases to show graphically the experiments. The results are summarized in the last section.