Intelligent agents offer a new and exciting way of understanding the world of work. Agent-Based Simulation (ABS), one way of using intelligent agents, carries great potential for progressing our understanding of management practices and how they link to retail performance. We have developed simulation models based on research by a multi-disciplinary team of economists, work psychologists and computer scientists. We will discuss our experiences of implementing these concepts working with a well-known retail department store. There is no doubt that management practices are linked to the performance of an organisation (Reynolds et al., 2005; Wall & Wood, 2005). Best practices have been developed, but when it comes down to the actual application of these guidelines considerable ambiguity remains regarding their effectiveness within particular contexts (Siebers et al., forthcoming a). Most Operational Research (OR) methods can only be used as analysis tools once management practices have been implemented. Often they are not very useful for giving answers to speculative 'what-if' questions, particularly when one is interested in the development of the system over time rather than just the state of the system at a certain point in time. Simulation can be used to analyse the operation of dynamic and stochastic systems. ABS is particularly useful when complex interactions between system entities exist, such as autonomous decision making or negotiation. In an ABS model the researcher explicitly describes the decision process of simulated actors at the micro level. Structures emerge at the macro level as a result of the actions of the agents and their interactions with other agents and the environment. 3 We will show how ABS experiments can deal with testing and optimising management practices such as training, empowerment or teamwork. Hence, questions such as "will staff setting their own break times improve performance?" can be investigated.

We propose a novel algorithm for greedy forward feature selection for regularized least-squares (RLS) regression and classification, also known as the least-squares support vector machine or ridge regression. The algorithm, which we call greedy RLS, starts from the empty feature set, and on each iteration adds the feature whose addition provides the best leave-one-out cross-validation performance. Our method is considerably faster than the previously proposed ones, since its time complexity is linear in the number of training examples, the number of features in the original data set, and the desired size of the set of selected features. Therefore, as a side effect we obtain a new training algorithm for learning sparse linear RLS predictors which can be used for large scale learning. This speed is possible due to matrix calculus based short-cuts for leave-one-out and feature addition. We experimentally demonstrate the scalability of our algorithm and its ability to find good quality feature sets.

The paper investigates parameterized approximate message-passing schemes that are based on bounded inference and are inspired by Pearls belief propagation algorithm (BP). We start with the bounded inference mini-clustering algorithm and then move to the iterative scheme called Iterative Join-Graph Propagation (IJGP), that combines both iteration and bounded inference. Algorithm IJGP belongs to the class of Generalized Belief Propagation algorithms, a framework that allowed connections with approximate algorithms from statistical physics and is shown empirically to surpass the performance of mini-clustering and belief propagation, as well as a number of other state-of-the-art algorithms on several classes of networks. We also provide insight into the accuracy of iterative BP and IJGP by relating these algorithms to well known classes of constraint propagation schemes.

RefereeToolbox is a java package implementing combination operators for fusing evidences. It is downloadable from: http://refereefunction.fredericdambreville.com/releases RefereeToolbox is based on an interpretation of the fusion rules by means of Referee Functions (refer to 2). This approach implies a dissociation between the definition of the combination and its actual implementation, which is common to all referee-based combinations. As a result, RefereeToolbox is designed with the aim to be generic and evolutive. It is composed of three distinct classes of objects: - A class for the logical representation of the information; this class is generic and incremental; are yet implemented structures as: - Powerset, - Free Boolean algebra, - Superpowerset, - Open/closed hyperpowerset, - · · · make yours! The generic implementation of RefereeToolbox makes possible to combine these three classes and their instances without restriction. For an introduction to the theory of referee function, please refer to [3]. A referee function for s sources of information is also called a s-ary referee function. The case X is called the rejection case. The value z is called the rejection rate. See the 19 GNU General P u b l i c License f o r more d e t a i l s. 20 21 You should have r e c e i v e d a copy o f the GNU General P ublic Lic e nse 22 along with RefereeToolbox. BR 50 BR 51 RefereeToolbox i s d i s t r i b u t e d i n the hope that i t w i l l be u s e f u l, 52 but WITHOUT ANY WARRANTY; without even the im plie d warranty o f 53 MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 54 GNU General P u b l i c License f o r more d e t a i l s. BR 55 BR 56 You should have r e c e i v e d a copy o f the GNU General P ublic Lic e nse 57 along with RefereeToolbox. See the 19 GNU General P u b l i c License f o r more d e t a i l s. 20 21 You should have r e c e i v e d a copy o f the GNU General P ublic Lic e nse 22 along with RefereeToolbox. BR 48 BR 49 RefereeToolbox i s d i s t r i b u t e d i n the hope that i t w i l l be u s e f u l, 50 but WITHOUT ANY WARRANTY; without even the im plie d warranty o f 51 MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 52 GNU General P u b l i c License f o r more d e t a i l s. BR 53 BR 54 You should have r e c e i v e d a copy o f the GNU General P ublic Lic e nse 55 along with RefereeToolbox. See the 19 GNU General P u b l i c License f o r more d e t a i l s. 20 21 You should have r e c e i v e d a copy o f the GNU General P ublic Lic e nse 22 along with RefereeToolbox. BR 45 BR 46 RefereeToolbox i s d i s t r i b u t e d i n the hope that i t w i l l be u s e f u l, 47 but WITHOUT ANY WARRANTY; without even the im plie d warranty o f 48 MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 49 GNU General P u b l i c License f o r more d e t a i l s.

To tackle the vocabulary problem in conversational systems, previous work has applied unsupervised learning approaches on co-occurring speech and eye gaze during interaction to automatically acquire new words. Although these approaches have shown promise, several issues related to human language behavior and human-machine conversation have not been addressed. First, psycholinguistic studies have shown certain temporal regularities between human eye movement and language production. While these regularities can potentially guide the acquisition process, they have not been incorporated in the previous unsupervised approaches. Second, conversational systems generally have an existing knowledge base about the domain and vocabulary. While the existing knowledge can potentially help bootstrap and constrain the acquired new words, it has not been incorporated in the previous models. Third, eye gaze could serve different functions in human-machine conversation. Some gaze streams may not be closely coupled with speech stream, and thus are potentially detrimental to word acquisition. Automated recognition of closely-coupled speech-gaze streams based on conversation context is important. To address these issues, we developed new approaches that incorporate user language behavior, domain knowledge, and conversation context in word acquisition. We evaluated these approaches in the context of situated dialogue in a virtual world. Our experimental results have shown that incorporating the above three types of contextual information significantly improves word acquisition performance.

We study online social networks in which relationships can be either positive (indicating relations such as friendship) or negative (indicating relations such as opposition or antagonism). Such a mix of positive and negative links arise in a variety of online settings; we study datasets from Epinions, Slashdot and Wikipedia. We find that the signs of links in the underlying social networks can be predicted with high accuracy, using models that generalize across this diverse range of sites. These models provide insight into some of the fundamental principles that drive the formation of signed links in networks, shedding light on theories of balance and status from social psychology; they also suggest social computing applications by which the attitude of one user toward another can be estimated from evidence provided by their relationships with other members of the surrounding social network.

In a previous paper the authors argued the case for incorporating ideas from innate immunity into articficial immune systems (AISs) and presented an outline for a conceptual framework for such systems. A number of key general properties observed in the biological innate and adaptive immune systems were hughlighted, and how such properties might be instantiated in artificial systems was discussed in detail. The next logical step is to take these ideas and build a software system with which AISs with these properties can be implemented and experimentally evaluated. This paper reports on the results of that step - the libtissue system.

Artificial immune systems (AISs) to date have generally been inspired by naive biological metaphors. This has limited the effectiveness of these systems. In this position paper two ways in which AISs could be made more biologically realistic are discussed. We propose that AISs should draw their inspiration from organisms which possess only innate immune systems, and that AISs should employ systemic models of the immune system to structure their overall design. An outline of plant and invertebrate immune systems is presented, and a number of contemporary systemic models are reviewed. The implications for interdisciplinary research that more biologically-realistic AISs could have is also discussed.

The field of artificial immune systems (AISs) is an emerging biologically-inspired method which builds systems based on algorithms inspired by the biological immune system. AIS research has provided a number of general purpose techniques and algorithms which have successfully been applied to a range of optimisation, classification and data mining problems. As with evolutionary algorithms and neural networks, AISs could also provide useful solutions to optimisation and classification problems in multi-sensor data fusion. More interestingly though perhaps, recent research in AISs [14,15,35,36] shows the importance of multilevel information in the construction of AISs. New models for AISs are emerging that are inspired by research in immunology into the role of the innate immune system in overall immune system dynamics. These AISs, which incorporate mechanisms inspired by both the innate and adaptive immune systems, are called second generation AISs. They stand in contrast to first generation AISs, which are inspired by adaptive immune system mechanisms only. One of the consequences of incorporating innate and adaptive mechanisms, as well as one of the defining characteristics of second generation AISs, is the need for a multilevel problem representation, and a multi-le- vel interaction of the components of the AIS with the problem [36]. As systems that integrate multilevel information sources, second generation AISs share much in common with multi-sensor data fusion systems.

Fatima Jinnah Women University Rawalpindi, Pakistan Abstract-Recent advancement in web services plays an important role in business to business and business to consumer interaction. Discovery mechanism is not only used to find a suitable service but also provides collaboration between service providers and consumers by using standard protocols. A static web service discovery mechanism is not only time consuming but requires continuous human interaction. This paper proposed an efficient dynamic web services discovery mechanism that can locate relevant and updated web services from service registries and repositories with timestamp based on indexing value and categorization for faster and efficient discovery of service. The proposed prototype focuses on quality of service issues and introduces concept of local cache, categorization of services, indexing mechanism, CSP (Constraint Satisfaction Problem) solver, aging and usage of translator. Performance of proposed framework is evaluated by implementing the algorithm and correctness of our method is shown. The results of proposed framework shows greater performance and accuracy in dynamic discovery mechanism of web services resolving the existing issues of flexibility, scalability, based on quality of service, and discovers updated and most relevant services with ease of usage. I. INTRODUCTION As an enabling technology, web services are software components that are used to present services on internet.