This paper considers the problem of patrolling multiple targets in a Euclidean environment by a single patrolling unit. We use game-theoretic approach and model the problem as a two-player zero-sum game in the extensive form. Based on the existing work in the domain of patrolling we propose a novel mathematical non-linear program for finding strategies in a discretized problem, in which we introduce a general concept of internal states of the patroller. We experimentally evaluate game value for the patroller for various graphs and strategy representations. The results suggest that adding internal states for the patroller yields better results in comparison to adding choice nodes in the used discretization.

Evolutionary language games are a way to study how perceptions, concepts, and language can emerge in populations of situated embodied agents, driven by the needs of communication and the properties of the environment. Evolutionary language games are currently being investigated using physical robots and this then requires that the full cycle of processing activities from physical robotic embodiment to sensory-motor processing, visual perception and action, conceptualization, and language processing are all integrated in a single system. This contribution reports on a large-scale long term effort to experiment with evolutionary language games and discusses major results achieved so far.

Natural world interaction (NWI), the pursuit of arbitrary goals in unstructured physical environments, is an excellent motivating problem for the reintegration of artificial intelligence. It is the problem set that humans struggle to solve. At a minimum it entails perception, learning, planning, and control, and can also involve language and social behavior. An agent's fitness in NWI is achieved by being able to perform a wide variety of tasks, rather than being able to excel at one. In an attempt to address NWI, a brain-emulating cognition and control architecture (BECCA) was developed. It uses a combination of feature creation and model-based reinforcement learning to capture structure in the environment in order to maximize reward. BECCA avoids making common assumptions about its world, such as stationarity, determinism, and the Markov assumption. BECCA has been demonstrated performing a set of tasks which is non-trivially broad, including a vision-based robotics task. Current development activity is focused on applying BECCA to the problem of general Search and Retrieve, a representative natural world interaction task.

This paper provides a formal analysis of a multi-agent task allocationproblem and how variation in agent behavior in the form of responseprobabilities can be used to build redundancy in the multi-agent system (MAS).In problems where experience is beneficial redundancy provides an MASwith a back-up pool of actors if the primary actors are unavailable.We examine how to ensure a complete team of agents needed fora particular task will be formed, as well as two different ways ofdetermining how to ensure some level of redundancy.

In all of these problems, we have limited be done; yet these are large-scale interdisciplinary research security resources which prevent full security coverage challenges that call upon multiagent researchers to work at all times; instead, limited security resources must be deployed with researchers in other disciplines, be "on the ground" intelligently taking into account differences in priorities with domain experts, and examine real-world constraints of targets requiring security coverage, the responses of and challenges that cannot be abstracted away. Together as the adversaries to the security posture and potential uncertainty an international community of multiagent researchers, we over the types, capabilities, knowledge and priorities can accomplish more! of adversaries faced.

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.

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).