The importance of diversity in reasoning and learning to successfully address complex problems is examined. We discuss an approach by which a multiagent framework with decentralized control mechanisms provides diverse perspectives and hypotheses addressing a class of complex problems. We introduce the SNARE multiagent system. SNARE performs tasks to gain situational awareness of situations of interest in a Social Media Space. It applies a decentralized control mechanism for each agent; this mechanism enables an agent to interact with other agents to reason and learn. This approach facilitates dynamic agent organizations that adapt the topologies of interactions between agents based on the problem context.

Traditional social science research has been unable to satisfactorily aggregate individual level data to group, organization and systems levels, making it one of social science’s biggest challenges (Giles, 2011). For game and social theory, we believe that the fault can be attributed to the lack of valid distance measures (e.g., the arbitrary ordering of cooperation and competition precludes a Hilbert space distance metric for the ordering of and gradations between these social behaviors, making game theory normative). Alternatively, we offer a theory of social interdependence with countable mathematics based on bistable or multi-stable perspectives and linear algebra. The evidence that is available is supportive. It indicates that meaning is a one-sided, stable, classical interpretation, not only making the correspondence between beliefs and objective reality in social settings incomplete, raising questioning about static theories from earlier eras (i.e., Axelrod’s evolution of cooperation; Simon’s bounded rationality). The result indicates for open systems (democracies) that interpretations evolve naturally to become orthogonal (Nash equilibria), that orthogonal interpretations generate the information to drive social evolution, but that in closed systems (dictatorships), dependent on the enforcement of social cooperation and the suppression of opposing points of view, evolution slows or stops (e.g., China, Iran or Cuba), causing capital and energy to be wasted, misdirected or misallocated as leaders suppress the interpretations that they alone have the authority to label as unethical, immoral, or irreligious. We conclude that a mathematics based on NE is feasible.

Robotic Sensor Networks composed of robots and wireless sensing devices hold the potential to revolutionize environmental sciences by enabling researchers to collect data across expansive environments, over long, sustained periods of time. We report progress on building such systems for two applications. The first application is on monitoring invasive fish (common carp) in inland lakes. In the second application, the robots act as data mules and collect data from sparsely deployed wireless sensors.

We present a statistical physics inspired approach to modeling, analysis, and design of distributed aggregation control policies for teams of homogeneous and heterogeneous robots. We assume high-level agent behavior can be described as a sequential composition of lower-level behavioral primitives. Aggregation or division of the collective into distinct clusters is achieved by developing a macroscopic description of the ensemble dynamics. The advantages of this approach are twofold: 1) the derivation of a low dimensional but highly predictive description of the collective dynamics and 2) a framework where interaction uncertainties between the low-level components can be explicitly modeled and control. Additionally, classical dynamical systems theory and control theoretic techniques can be used to analyze and shape the collective dynamics of the system. We consider the aggregation problem for homogeneous agents into clusters located at distinct regions in the workspace and discuss the extension to heterogeneous teams of autonomous agents. We show how a macroscopic model of the aggregation dynamics can be derived from agent-level behaviors and discuss the synthesis of distributed coordination strategies in the presence of uncertainty.

This would allow teams to have an objective currency for trust transactions. These systems would allow another surface for autonomous Ali, A.S., Rana, O., and Walker, D.W. (2004): "WS-QoC: agents to integrate the social fabric with information Measuring Quality of Service Compliance," International gathered in virtual environments. Further, the system would Conference on Service Oriented Computing (ICSOC04), New increase illumination of dark networks engaged in illicit York, NY. covert activity. Participants would be assigned a score Allbeck, J., and Badler, N. (2002): "Toward Representing Agent similar to FICO scores; when an individual score falls Behaviors Modified by Personality and Emotion," Autonomous noticeably or falls below a threshold, further observation Agents and Multiagent Systems, Bologna, Italy.

Nexus Network Learner is an intelligent agent based simulation used to study Irregular Warfare (IW) in several major studies at the Department of Defense (DoD). Heterogeneous autonomous agents, each with their own separated inductive learning mechanism, have initial attributes and behaviors in proportion to demographic groups in the simulated population, and learn new behaviors as they serve culturally based goals. Nexus agents create a dynamic role-based network, and learn how to choose partners as well as what behaviors they should have with their network partners. As Nexus agents coevolve, nexus models the emergence of social institutions from individual behaviors, the fundamental social aggregation challenge. Nexus models the formation of learned vicious and virtuous cycles of behavior, some of which have higher average utility for the agents than others, and can be used to test the effects of interventions on the natural motivation-based system. An experiment is presented that uses Nexus to model the vicious cycle of corruption in an African country, from the first Irregular Warfare Analytical baseline at the Office of the Secretary of Defense (Messer 2009).

When market works and when it fails has been an issue long pursued by economists. While to an extreme extent the view, as characterized by the “invisible hand” or “market mechanism”, has been so dominant in economics education and public policy debates, it is generally acceptable that markets are not out there and have to designed properly so as to work (McMillan, 2004). The significance of designs has been further illustrated by experimental economics. As opposed to designs, what, however, has been drawn less attention is the role of traders, their characteristics and behavior. To one extreme, one may consider that a good design is so dominant that there leaves little room for individual traders to play a role. The literature inspired by the zero-intelligence agent (Gode and Sunder, 1993) provides a good background of this issue, and many later studies do cast doubt on the sufficiency of this minimal intelligence and propose different versions of additional intelligence.  

Forecasting the behavior of systems with multiple responses has been a challenging problem in the context of many applications such as collaborative filtering, financial markets, or bioinformatics, where responses may be, respectively, movie ratings, stock prices, or activity of genes within a cell. Statistical modeling techniques have been widely applied for learning multivariate time series either in the multiple linear regression setting [3] or with autoregressive models [19]. More recently, kernel-based regularized methods have been developed for multitask learning [7, 2]. These approaches share in common the use of the correlation structure between input variables to enhance prediction of every single output. Frequently, the correlation structure is assumed to be given or is estimated separately.

In many instances, information on engineering systems can be obtained through measurements, monitoring or direct observations of system performances and can be used to update the system reliability estimate. In structural reliability analysis, such information is expressed either by inequalities (e.g. for the observation that no defect is present) or by equalities (e.g. for quantitative measurements of system characteristics). When information Z is of the equality type, the a-priori probability of Z is zero and most structural reliability methods (SRM) are not directly applicable to the computation of the updated reliability. Hitherto, the computation of the reliability of engineering systems conditional on equality information was performed through first- and second order approximations. In this paper, it is shown how equality information can be transformed into inequality information, which enables reliability updating by solving a standard structural system reliability problem. This approach enables the use of any SRM, including those based on simulation, for reliability updating with equality information. It is demonstrated on three numerical examples, including an application to fatigue reliability.

Individuals and groups, within organisations, cooperate by producing, manipulating and organizing knowledge, and by building and refining new collective knowledge. Organisations increasingly see their intellectual capital as strategic resources that must be managed effectively to achieve competitive advantage. This capital consists of the knowledge held in the minds of its members, embodied in its procedures and decision making processes, and stored in its repositories. Subsequently, it should be useful for KM systems and Collaboration systems to integrate both kinds of capabilities into a single collaborative-and-knowledge based system to support joint efforts towards a goal [1]. Decision making is one of the critical processes where we need both knowledge management (that focuses on creation, storage, sharing and use of knowledge) and collaboration (that focuses on cooperation, communication, coordination and coproduction) to make that more effective and efficient. This paper aims to explicit step-by-step the multimodal decision making (MDM) process at three levels (individual, collective and hybrid) and is organized as follows; we start with a brief overview of the literature on collaborative knowledge management. In section three, we propose formal description of MDM process. Finally, section four presents our model of MDM process basing on the proposed formal description and UML-G profile.