Agent Based

We use an agent-based approach to model trading behaviour in high-frequency markets. This study focuses on the Foreign Exchange (FX) market. The initial part of this study is to observe the micro-behaviour of traders to define the stylized facts of their trading activities. This is performed using a high- frequency dataset of anonymised individual traders' historical transactions on an account

The problem of mobile robot navigation has received a noticeable attention over last few years. Several different approaches were presented, each having major limitations. In this paper a new, agentbased solution the problem of mobile robots navigation is proposed. It is based on a novel representation of the environment, that divides it into a number of distinct regions, and assigns autonomous software Space Agents to supervise it. Space Agents create a graph, that represents a high-level structure of the entire environment. The graph is used as a virtual space, that robot controlling agents work in. The most important features of the approach are: path planning for multiple robots based on most recent data available in the system, automated collision avoidance, simple localization of a "lost robot" and unrestricted scalability.

Despite the advances in argumentation on group decision negotiation there is a need to simulate and identify the personality of participants. To make participants' agents more human-like and to increase their flexibility in the negotiation process in group decision-making, the authors investigated the role of personality behaviours of participants applied to the conflict style theme. The negotiation is made in a bilateral way where both parties are OCEAN participant agents based on the five-factor model of personality (Openness, Conscientiousness, Extraversion, Agreeableness and Negative emotionality).

This paper describes the development of a generalized agent -based simulation tool for studying manpower and personnel management behaviors. The subject matter of the simulation are complex organizational behaviors found within Uni ted States Navy Manpower and Personnel processes. Behavior of such a complex system is typically associated with a hierarchi- cal structure in which the lowest level agents are

Wireless sensor networks (WSNs) have been identified as a promising technology that will allow people and machines to interact with their environment in a revolutionary way. These networks, however, are facing limitations such as energy constraints of the sensor and difficulties in reprogramming the actual network. To address these limitations we propose a novel agent middleware. Namely In-Motes can be considered as an intelligent network which is deployed with no pre-installed application. Mobile agents are injected into the network, then migrate and clone across it, following specific rules and performing application specific tasks. By doing so, each mote is given a certain degree of perception, cognition and control, forming the basis of its intelligence. Linda-like tuplespaces and federated system architecture are proposed as the means for collaboration and coordination of the agents. In order to make the network more robust, certain behavioural rules are proposed taking inspiration from a community of bacterial strains. These preserve each agent’s certain degree of autonomy and identifies a highly coordinated architecture for them.

Searching and comparing products satisfying buyer's preferences is one of the essential activities in ecommerce. It is difficult when product space is large and multiple criteria on the product features are to be considered. Furthermore, the matchmaking becomes a challenging problem if buyer's preferences are fuzzy. In this paper, we present a linguistic approach to compare and rank products based on fuzzy preferences, thus making e-commerce more pleasurable for both the buyer and the seller in a marketplace. Our experimental results show the approach approximates human reasoning and is very encouraging.

The use of software agents as Database Management System components lead to database systems that may be conflgured and extended to support new requirements. We focus here with the self-tuning feature, which demands a somewhat intelligent behavior that agents could add to traditional DBMS modules. We propose in this paper an agent-based database architecture to deal with automatic index creation.

Ahsrrcict-Th is paper proposes I IP(I ntel I igent Internet Protocol) which effectively addresses the issues involved in wireless communication at Network Layer such a s support for mobility, frequent loss of connection due to fading, handoffs and security. We have chosen to use Intelligent Agent technology for our architecture of 1IP because it provides desirable properties such as Speed-up and Efficiency, Robustness and Reliability, Scalability and Flexibility, Development and Re-usability. We make use of .four Intelligent Agents namely Principal Agent(PA), Agent for Address Resolution and Mobility Support(AARMS), Agent for Error Handling and Recovcry(AEHR), Agent for Network Security(ANS) and design each Agent bused on BDI(Belief-Desire-Intention) Architecture. A variant of Blackboard architecture is used for inter-agent communication. Special carc has been taken to make proposed IP compatible with existing Networks. The proposed work has been simulated and verified for its operational effectiveness.

This paper illustrates a new approach to the problem of making a logical network resource configuration, adapt to customer utilisation. This is to be achieved through the use of distributed multi-agent based control techniques. The agents have goals derived from different quality metrics, which the network has to provide. In ATM networks a well designed Virtual Path Connection (VPC) overlay network tries to maximise the probability of being able to accommodate connection demand within the control plane for that particular time frame. We assume that there already exists a reasonable VPC route topology in operation. The distributed agent control architecture (Tele-MACS) describes how the coupling of planning agents with reactive agents can adapt the network resource configuration to comply with changes in user demand. The changes to the VPC route topology or capacity assignments are a requirement for the network to adapt to user behaviour and our control architecture demonstrates a mechanism for achieving the reconfigurations. The network scenario is considered a closed system, where the network operator has full control over the network elements, such as the routing tables and switching capabilities. We demonstrate our concepts with a focus on a core node ATM wide area network example, where the control system seeks to maintain network survivability and efficient use of bandwidth resources.

Because of new economical challenges and recent trends regarding international trade and globalization, many companies from the Canadian forest products industry have reached the point where profit improvement cannot be reaped without the coordinated involvement of their entire organization. Such a new level of efficiency concerns their distributed facilities and offices spread around the world, as well as their customers. One consequence of this new reality is that forest products companies are now facing the need to reengineer their organizational processes and business practices with their partners. To do this they must adopt new technologies to support the coordination of their planning and control efforts in a customer-centered environment. This paper first proposes a generic software architecture for the development of an experimentation environment to design and test distributed advanced planning and scheduling systems. This architecture allows combining agent-based technology and operations researchbased tools in order to take advantage, on the one hand, of the ability of agent technology to integrate distributed decision problems, and, one the other hand, the ability of operations research to develop and exploit specific normative decision models. Next, this paper describes how this architecture has been configured into an advanced planning and scheduling tool for the lumber industry. Finally, we present how an application of this advanced planning tool is currently being validated and tested in a real manufacturing setting.

Using intelligent agent-based systems to support information processing for executives has not been significantly advanced in both theory and practice. Research into this field tends to focus more on technical aspects than on social perspective. When executives are faced with increasing information availability and uncertainty in the business environment, using intelligent agent-based systems to enhance executives’ information processing capability appears

Evolutionary game models normally have a finite set of strategies. Recently, evo- lutionary games with continuous spaces of strategies have been introduced by Dan Friedman and Joel Yellin, under the name landscape dynamics. In one example extensively studied by them, conspicuous consumption, a simple (gradient) dynamical system is introduced on the strategy space. In this article, we translate a landscape dynamic system into a complex dy- namical system, and then into an agent based model. The conspicuous consumption model of Friedman and Yellin is realized as an agent based model in NetLogo to illustrate the concepts of this new modeling strategy.

Manufacturing control systems are a mission-critical application domain for semantic agents systems. While multi-agent systems have been explored in the manufacturing systems domain, there is very little work on semantically enabled agent systems. This chapter introduces a layered architecture for manufacturing systems based on agent systems and discusses relevant capabilities of the semantic agents based on real-world use cases.

We introduce a simple model for simulating financial markets, based on an order book, in which several agents trade one asset at a virtual exchange continuously. For a stationary market the structure of the model, the order flow rates of the different kinds of order types and the used price time priority matching algorithm produce only a diffusive price behavior. We show that a market trend, i.e. an asymmetric order flow of any type, leads to a non-trivial Hurst exponent for the price development, but not to "fat-tailed" return distributions. When one additionally couples the order entry depth to the prevailing trend, also the stylized empirical fact of "fat tails" can be reproduced by our Order Book Model.

Knowledge sharing in organizations, especially the impact of sharing freely versus not sharing, was studied using game theoretic analysis and a Netlogo agent-based simulation model. In both analyses, some agents hoarded knowledge while others shared knowledge freely. As expected, sharing was found to greatly increase the overall amount of knowledge within the organization. Unexpectedly, on average, agents who share acquire more knowledge than hoarders. This is in contradiction to the conclusion from the prisoner's dilemma analysis. This is due to the synergy that develops between groups of agents who are sharing with each other. The density of the agents is important; as the density increases, the probability increases that an agent with a large amount of knowledge to share happens to be organizationally nearby. The implications are that organizations should actively encourage knowledge sharing, and that agent-based simulation is a useful tool for studying this type of organizational phenomena.

We describe an approach to investigating design cognition which involved comparing prescriptive theories of good design practice with observations of actual design behaviour. The tenet of prescriptive theory which formed the focus of the research is the idea that designers should generate and evaluate multiple design alternatives in order to increase the chances of attaining better design solutions than might arise if they fixated upon an initial solution. Our study focused upon six professional electronic engineers attempting a novel integrated-circuit design problem. Verbal-protocol data revealed: (i) a failure to search for alternative solutions; (ii) a marked inclination to stick with early 'satisficing' solution ideas even when these were showing deficiencies; and (iii) only superficial modelling and assessment of competing alternatives when such options were actually considered. We argue that while minimal solution search in design may sometimes be caused by motivational factors and working-memory limitations, its major determinant relates to inhibitory memory processes that arise subsequent to the recognition-based emergence of familiar design solutions. We conclude by exploring the implications of minimal solution search for design support, with particular reference to an agent-based indexing system which we are developing in order to facilitate the pursuit of design alternatives in engineering contexts. 0 1998 Published by Elsevier Science B.V. solving expertise in general as well as of design expertise in particular [ll. At an applied level, an analysis of solution-development processes in design is vital for devising optimal methods for design education and sound techniques for computer-based design support [2]. A general approach to investigating goal-directed problem-solving processes which has been applied to good effect in many areas of cognitive psychology is to compare normative theories of optimal practice with what people actually do. For example, numerous investigations of decision making (e.g., Ref.

A comprehensive approach to the design and implementation of multi-robots cooperative systems is described. It focuses on a design process that uses the Unified Modeling Language and on a detailed ontology description with the goal of sharing the knowledge on environments that robots can acquire through the use of their vision sub-system. We base the implementation of our robotics vision system on agents inserted in a generic multi-level architectures. The first objective of this work is to provide a framework ...

Abstract This paper introduces a multi-agent framework for distributed multiplayer game development and simulations. The framework allows the incorporation and experimentation with different strategies of the Players, possibly including intelligent behaviors. The framework consists of a set of generic agent types that must be specialized and configured for a specific game. The framework uses a generic agent that enforces the rules of the game, as well as generic bot-agents that represent the game Players. To support our claims of ...

This is an attempt at a succinct survey, from methodological and epistemological perspectives, of the burgeoning, apparently unstructured, field of what is often – misleadingly – referred to as computational economics. We identify and characterise four frontier research fields, encompassing both micro and macro aspects of economic theory, where machine computation play crucial roles in formal modelling exercises: algorithmic behavioural economics, computable general equilibrium theory, agent based computational economics and computable economics. In some senses these four research frontiers raise, without resolving, many interesting methodological and epistemological issues in economic theorising in (alternative) mathematical modes

The paper presents a semantic logging framework which allows structured information logging in an agent-based distributed system for chemical incident response. The logging framework is "semantic" because it allows semantic interpretation of logs according to relationships defined between logging events. For example, this approach could help the reconstruction of the order of events that occurred during the response to an incident, thus giving a detailed view of system execution trace, as well as of agents' decisions taken at various decision points during the incident management workflow. We intend to use semantic logs (i) for helping experts to analyze and explain system actions and thus improving system response to future incidents, as well as (ii) for training stakeholders by setting the system to run replaylike simulations of past incident management workflows. Sorin Ilie, Mihnea Scafeş, Costin Bȃdicȃ, Thomas Neidhart, and Rani Pinchuk were supported by DIADEM project. Diadem project is funded by European Union under Information and Communication Technologies (ICT) theme of the 7th Framework Programme for R&D, ref. no: 224318. 1. DIADEM project -Distributed information acquisition and decisionmaking for environmental management: http://www.ist-diadem.eu/ -265 -978-1-4244-7433-2/10/$26.00 ©2010 IEEE

We study an early learning of object grasping process where the agent, based on a set of innate reflexes and knowledge about its embodiment. We present a system that extracts low-level 3D visual features from a binocular vision system and uses geometry, appearance and spatial relations between the features to guide early reactive grasping.

In this paper we compare two alternative theoretical approaches for simulating the growth of cell aggregates in vitro: individual cell (agent)-based models and continuum models. We show by a quantitative analysis of both a biophysical agent-based and a continuum mechanical model that for densely packed aggregates the expansion of the cell population is dominated by cell proliferation controlled by mechanical stress. The biophysical agent-based model introduced earlier (Drasdo and Hoehme in Phys Biol 2:133-147, 2005) approximates each cell as an isotropic, homogeneous, elastic, spherical object parameterised by measurable biophysical and cell-biological quantities and has been shown by comparison to experimental findings to explain the growth patterns of dense monolayers and multicellular spheroids. Both models exhibit the same growth kinetics, with initial exponential growth of the population size and aggregate diameter followed by linear growth of the diameter and power-law growth of the cell population size. Very sparse monolayers can be explained by a very small or absent cell-cell adhesion and large random cell migration. In this case the expansion speed is not controlled by mechanical stress but by random cell migration and can be modelled by the Fisher-Kolmogorov-Petrovskii-Piskounov (FKPP) reaction-diffusion equation. The growth kinetics differs from that of densely packed aggregates in that the initial spread, as quantified by the radius of gyration, is

We propose a new approach towards particle swarm optimization named agent-based PSO. The swarm is elevated to the status of a multi-agent system by giving the particles more autonomy, an asynchronous execution, and superior learning capabilities. The problem space is modeled as an environment which forms clusters of points that are known to be non-optimal and this transforms the environment into a more dynamic and informative resource

The vision of a smart grid is predicated upon pervasive use of modern digital communication techniques to today's power system. As wide area measurements and control techniques are being developed and deployed for a more resilient power system, the role of communication network is becoming prominent. Power system dynamics gets influenced by the communication delays in the network. Therefore, extensive integration of power system and its communication infrastructure mandates that the two systems are studied as a single distributed cyber-physical system. This paper proposes a power/network co-simulation framework which integrates power system dynamic simulator and network simulator together using an accurate synchronization mechanism. The accuracy is tunable based on the time-scale requirements of the phenomena being studied. This co-simulation can improve the practical investigation of smart grid and evaluate wide area measurement and control schemes. As a case study an agent-based remote backup relay system is simulated and validated on this cosimulation framework.

Increasing deployment density and shrinking size of wireless sensor nodes requires small equipped battery size. This means emerging wireless sensor nodes must compete for efficient energy utilization. Medium Access Control (MAC) protocols play a vital role in energy consumption of sensor node as it controls the radio activities. Customized or open source simulators play an important role to measure the performance effectiveness of MAC protocols based on the fact that they are flexible, reduce experimental overhead and cost. Nevertheless, these benefits come at the cost of results accuracy. In this paper, we investigate differences of the behaviour of our agent based S-MAC protocols in real deployment compared to the results produced using our custom based simulator, which ignores the lower layers effects such as packet collision and overhearing. We use network simulator 2 (ns2), an open source simulator, which provides a complete protocol stack. We further try to find and explain the rationale of the variance of results produced by real deployment and that of simulators.

This paper addresses the problem of predicting and evaluating the impacts of the built environment on its human inhabitants. It presents a simulation system comprising a usabilitybased building model and an agent-based virtual user model. The building model represents both geometric information and usability properties of design elements, and is generated automatically from a standard CAD model. Virtual users are modelled as autonomous agents that emulate the appearance, perception, social traits and physical behaviour of real users (walking, sitting, meeting other virtual users, etc.). Their behaviour model is based upon theoretical and practical environment-behaviour studies, real world data from a field study, and Artificial Life research. By inserting the virtual users in the usability-enabled building model, and letting them "explore" it on their own volition, the system reveals the interrelationship between the environment and its users. The environment can then be modified, to see how different arrangements affect user behaviours.

This article presents Gaia: a methodology for agent-oriented analysis and design. The Gaia methodology is both general, in that it is applicable to a wide range of multi-agent systems, and comprehensive, in that it deals with both the macro-level (societal) and the micro-level (agent) aspects of systems. Gaia is founded on the view of a multi-agent system as a computational organisation consisting of various interacting roles. We illustrate Gaia through a case study (an agent-based business process management system).

In this paper we develop a Ms. Pac-Man playing agent based on an influence map model. The proposed model is as simple as possible while capturing the essentials of the game. Our model has three main parameters that have an intuitive relationship to the agent's behavior. Experimental results are presented exploring the model's performance over its parameter space using random and systematic global exploration and a greedy algorithm. The model parameters can be optimized without difficulty despite the noisy fitness function used. The performance of the optimized agents is comparable to the best published results for a Ms. Pac-Man playing agent. Nevertheless, some difficulties were observed in terms of the model and the software system.

The contribution presents a multi-agent technology in the domain of the air-traffic control of several autonomous aerial vehicles (manned as well as unmanned). The system has been validated mainly by the collision avoidance (CA) tasks. Several cooperative and non-cooperative CA methods have been integrated in the system to validate and compare their properties in the scalable experiments. The AGENTFLY system is suitable also for the developing and testing of algorithms for the collective flight control. The operation of the underlying multi-agent system has been integrated with freely available geographical and tactical data sources. The system provides real time 2D/3D visualization and also the web-access component.

One of the key issues in development of Multi-agent System(MAS) is to follow the more suitable development process for a particular problem. This means that development process models must be defined. Definition leads to better process understanding, facilitates process measurement and improvement, and constitutes the basis for automating the process itself. The main goal of this paper is the definition of a process for agent-based development. This definition is based on the Software Process Engineering Metamodel(SPEM) and applied in the definition of the software engineering process of INGENIAS MAS methodology following a SCRUM approach.

The paper provides an overview of the agent-based solutions developed by the Rockwell Automation company for the purposes of industrial control. Using agent-based manufacturing control, a higher degree of flexibility and reconfigurability of manufacturing solutions as well as higher robustness of the industrial systems can be achieved. Specific solutions connected with the proposed agent architecture, with implementation of the real-time control agents as well as with the information transfer among the SW agents and the real-time agents in a Programmable Logic Controller (PLC) are presented. Attention is also paid to the simulation of both the agent-based manufacturing facilities and their control systems. A simulation environment MAST for material handling systems has been implemented in JADE. The opportunity to re-use directly the simulation software on the agent control level is one of the most important features of MAST.

The differential equation of Abrams and Strogatz for the competition between two languages is compared with agentbased Monte Carlo simulations for fully connected networks as well as for lattices in one, two and three dimensions, with up to 10 9 agents. In the case of socially equivalent languages, agent-based models and a mean-field approximation give grossly different results. r

In recent years, the noticeable move towards using mobile devices (mobile phones and PDAs) and wireless technologies have made information available in the context of "anytime, anywhere using any mobile device" experience. Delivering information to ...

Literature on the modeling and simulation of complex adaptive systems (cas) has primarily advanced vertically in different scientific domains with scientists developing a variety of domain-specific approaches and applications. However, while cas researchers are inherently interested in an interdisciplinary comparison of models, to the best of our knowledge, there is currently no single unified framework for facilitating the development, comparison, communication and validation of models across different scientific domains. In this thesis, we propose first steps towards such a unified framework using a combination of agent-based and complex network-based modeling approaches and guidelines formulated in the form of a set of four levels of usage, which allow multidisciplinary researchers to adopt a suitable framework level on the basis of available data types, their research study objectives and expected outcomes, thus allowing them to better plan and conduct their respective research case studies. Firstly, the complex network modeling level of the proposed framework entails the development of appropriate complex network models for the case where interaction data of cas components is available, with the aim of detecting emergent patterns in the cas under study. The exploratory agent-based modeling level of the proposed framework allows for the development of proof-of-concept models for the cas system, primarily for purposes of exploring feasibility of further research. Descriptive agent-based modeling level of the proposed framework allows for the use of a formal step-by-step approach for developing agent-based models coupled with a quantitative complex network and pseudocode-based specification of the model, which will, in turn, facilitate interdisciplinary cas model comparison and knowledge transfer. Finally, the validated agent-based modeling level of the proposed framework is concerned with the building of in-simulation verification and validation of agent-based models using a proposed Virtual Overlay Multiagent System approach for use in a systematic team-oriented approach to developing models. The proposed framework is evaluated and validated using seven detailed case study examples selected from various scientific domains including ecology, social sciences and a range of complex adaptive communication networks. The successful case studies demonstrate the potential of the framework in appealing to multidisciplinary researchers as a methodological approach to the modeling and simulation of cas by facilitating effective communication and knowledge transfer across scientific disciplines without the requirement of extensive learning curves.

Modern prosperous cities strongly need advanced parking assistant systems, intelligent transportation systems providing drivers with parking information. Existing parking information systems usually ignore the parking price factor and do not automatically provide optimal car parks matching drivers' demand. Currently, the parking price has no negotiable space; consumers lose their bargaining position to obtain better and cheaper parking. This study uses an intelligent agent system, and considering negotiable parking prices, selects the optimal car park for the driver. The autonomous coordination activities challenge traditional approaches and call for new paradigms and supporting middleware. An agent-based coordination network is proposed to bring true benefit to drivers and car park operators. These modern intelligent agents have capabilities including planning, mobility, execution monitoring and coordination. These properties can be used to construct the integrated parking assistant system.

This paper reviews the field of artificial intelligence focusing on embodied artificial intelligence. It also considers models of artificial consciousness, agent-based artificial intelligence and the philosophical commentary on artificial intelligence. It concludes that there is almost no consensus nor formalism in the field and that the achievements of the field are meager.

Abstract—We investigate the stability properties of a novel agent-based system for the detection of network bandwidth-based distributed denial of service (DDoS) attacks. The pro-posed system provides a description of the structure of flows which comprise the DDoS attack. In doing ...

Agent-based modeling and simulation (ABMS) is a new approach to modeling systems comprised of autonomous, interacting agents. Computational advances have made possible a growing number of agent-based models across a variety of application domains. Applications range from modeling agent behavior in the stock market, supply chains, and consumer markets, to predicting the spread of epidemics, mitigating the threat of bio-warfare, and understanding the factors that may be responsible for the fall of ancient civilizations. Such progress suggests the potential of ABMS to have far-reaching effects on the way that businesses use computers to support decision-making and researchers use agent-based models as electronic laboratories. Some contend that ABMS "is a third way of doing science" and could augment traditional deductive and inductive reasoning as discovery methods. This brief tutorial introduces agent-based modeling by describing the foundations of ABMS, discussing some illustrative applications, and addressing toolkits and methods for developing agent-based models.

Agent-based modelling has become an important tool to investigate socio-ecological processes. Its use is partially driven by increasing demand from decision makers to provide support for understanding the potential implications of decisions in complex situations. While one of the advantages of agent-based modelling is the ability to simulate the implications of human decision-making processes explicitly, methods for providing empirical support for the representation of the behaviour of human agents have not been structured systematically. This paper develops a framework for the parameterisation of human behaviour in agent-based models and develops twelve distinct sequences for the characterisation and parameterisation of human behaviours. Examples are provided to illustrate the most important sequences. This framework is a first step towards a guide for parameterisation of human behaviour in ABM. A structured discussion within the agent-based community is needed to achieve a more definitive guideline.

For years, the field of drug delivery has focused on (1) controlling the release of a therapeutic and (2) targeting the therapeutic to a specific cell type. These research endeavors have concentrated mainly on the development of new degradable polymers and molecule-labeled drug delivery vehicles. Recent interest in biomaterials that respond to their environment have opened new methods to trigger the release of drugs and localize the therapeutic within a particular site. These novel biomaterials, usually termed "smart" or "intelligent", are able to deliver a therapeutic agent based on either environmental cues or a remote stimulus. Stimuli-responsive materials could potentially elicit a therapeutically effective dose without adverse side effects. Polymers responding to different stimuli, such as pH, light, temperature, ultrasound, magnetism, or biomolecules have been investigated as potential drug delivery vehicles. This review describes the most recent advances in "smart" drug delivery systems that respond to one or multiple stimuli.

The aim of this paper is that of describing the software architecture designed for the implementation of an agent programming framework. Contrary to the widely adopted use of the Java platform in most agent-based solutions and related research activity, the present work has been entirely carried on in the novel .NET framework (Microsoft Corp.) which offers technological solutions able to cope with common problems which often arise when working in other enterprise platforms (both J2EE and Microsoft DNA). In particular this work is based on the C# language and the Common Language Infrastructure. One solution is described to make management of agents easier from a programmer's point of view. As result an agent programming framework is presented, fully exploiting the above concepts.

This research aims to develop a model of trust and reputation that will ensure good interactions amongst software agents in large scale open systems. The following are key drivers for our model: (1) agents may be self-interested and may provide false accounts of experiences with other agents if it is beneficial for them to do so; (2) agents will need to interact with other agents with which they have little or no past experience. Against this background, we have developed TRAVOS (Trust and Reputation model for Agentbased Virtual OrganisationS) which models an agent's trust in an interaction partner. Specifically, trust is calculated using probability theory taking account of past interactions between agents. When there is a lack of personal experience between agents, the model draws upon reputation information gathered from third parties. In this latter case, we pay particular attention to handling the possibility that reputation information may be inaccurate.

If a typhoon in the South China Sea impacts the shipment and delivery of memory chips to an assembly plant in Mexico City, you can count on the ripple effect to impact financial service providers, manufacturers and suppliers, shippers in charge of logistics and of course, the end-consumer. Can we plan to reduce the risk arising from such uncertainties? Can businesses (semiconductor plants, banks, logistics providers) cooperate to minimize uncertainties? Conventional wisdom states that uncertainties are equivalent to accidents and hence by nature remain unpredictable. However, application of tools and technologies based on emerging standards may partially disprove such wisdom. Focus on demand management may be the guiding light for supply chain practitioners. Can we collapse information asymmetries (between manufacturers and their lending institutions, for example) and add far more value to networks or demand webs? Real-time operational adaptability is key, especially in fast 'clockspeed' industries. Confluence of emerging tools, technologies and standards are required to converge to catalyze the evolution of such adaptable enterprise. Can real-time distributed data, in-network processing, Agent-based autonomy, taken together, tame the Bullwhip Effect? Can the (semantic) web catalyze the "Nash Equilibrium" of people (games) and information (theory) in our quest for real time "predictive" decision support systems? We will explore a few of these issues and how they may coalesce to enable the adaptive value network of the future.

Recent progress in an emerging area of designing aptamer and nanomaterial conjugates as molecular diagnostic and drug delivery agents in biomedical applications is summarized. Aptamers specific for a wide range of targets are first introduced and compared to antibodies. Methods of integrating these aptamers with a variety of nanomaterials, such as gold nanoparticles, quantum dots, carbon nanotubes, and superparamagnetic iron oxide nanoparticles, each with unique optical, magnetic, and electrochemical properties, are reviewed. Applications of these systems as fluorescent, colorimetric, magnetic resonance imaging, and electrochemical sensors in medical diagnostics are given, along with new applications as smart drug delivery agents.