This paper presents the results of computational experiments on the effects of social influence on individual and systemic behavior of situated cognitive agents in a product-consumer environment. Paired experiments were performed with identical initial conditions to compare social agents with non- social agents. Experiment results show that social agents are more productive in consuming available products, both in terms of aggregate unit consumption and aggregate utility. But this comes at a cost of individual average utility per unit consumed. In effect, social interaction achieved higher productivity by 'lowering the standards' of individual consumers. While still at an early stage of development, such an agent-based model laboratory is shown to be an effective research tool to investigate rich collective behavior in the context of demanding cognitive tasks.

Agent technology is a software paradigm that permits to implement large and complex distributed applications. In order to assist analyzing, conception and development or implementation phases of multi-agent systems, we've tried to present a practical application of a generic and scalable method of a MAS with a component-oriented architecture and agent-based approach that allows MDA to generate source code from a given model. We've designed on AUML the class diagrams as a class meta-model of different agents of a MAS. Then we generated the source code of the models developed using an open source tool called AndroMDA. This agent-based and evolutive approach enhances the modularity and genericity developments and promotes their reusability in future developments. This property distinguishes our design methodology of existing methodologies in that it is constrained by any particular agent-based model while providing a library of generic models

The paper presents an ant colony optimization metaheuristic for collaborative planning. Collaborative planning is used to coordinate individual plans of self-interested decision makers with private information in order to increase the overall benefit of the coalition. The method consists of a new search graph based on encoded solutions. Distributed and private information is integrated via voting mechanisms and via a simple but effective collaborative local search procedure. The approach is applied to a distributed variant of the multi-level lot-sizing problem and evaluated by means of 352 benchmark instances from the literature. The proposed approach clearly outperforms existing approaches on the sets of medium and large sized instances. While the best method in the literature so far achieves an average deviation from the best known non-distributed solutions of 46 percent for the set of the largest instances, for example, the presented approach reduces the average deviation to only 5 percent.

In the problem of task allocation, form of probabilistic response tendencies can be used to redundancy refers to extra agents beyond the minimum achieve redundancy when an MAS is working on a problem number of required agents that have the capability to perform in which experience is beneficial. We assume that the MAS a given task. Particularly in problems where experience is a response threshold system (Bonabeau, Theraulaz, and is beneficial, redundancy provides an MAS with a Deneubourg 1998) and that previous experience on a task backup pool of ready actors if the primary actors are unavailable improves an agent's future performance on that task.

Hybrid group autonomy, organizations and teams composed of humans, machines and robots, are important to AI. Unlike the war in Iraq in 2002, the war in Afghanistan has hundreds of mobile robots aloft, on land, or under the sea. But when it comes to solving problems as part of a team, these agents are socially passive. Were the problem of aggregation and the autonomy of hybrids to be solved, robot teams could accompa- ny humans to address and solve problems together on Mars, under the sea, or in dan- gerous locations on earth (such as, fire-fighting, reactor meltdowns, and future wars). “Robot autonomy is required because one soldier cannot control several robots ... [and] because no computational system can discriminate between combatants and innocents in a close-contact encounter.” (Sharkey, 2008) Yet, today, one of the fundamental unsolved problems in the social sciences is the aggregation of individual data (such as preferences) into group (team) data (Giles, 2011) The original motivation behind game theory was to study the effect that multi- ple agents have on each other (Von Neumann and Morgenstern, 1953), known as interdependence or mutual dependence. Essentially, the challenge addresses the ques- tion: why is a group different from the collection of individuals who comprise the group? That the problem remains unsolved almost 70 years later is a remarkable com- ment on the state of the social sciences today, including game theory and economics. But solving this challenge is essential for the science and engineering of multiagent, multirobot and hybrid environments (that is, humans, machines and robots working together).

Computational models of aggregated social agents have two major faults: (1) inter-individual entrainment is ignored; and (2) rule-sets governing behavior are invariant to history. Together these shortcomings impede our ability to generate realistic models of complex evolving social processes. To illustrate how even simple couplings within an established dyad generates unexpected outcomes, we present our findings from two computer models (agent-based, particle filter) of married couples. With the use of computational modeling, especially when attempting to capture and articulate trajectories of socially aggregated agents, numerous implicit assumptions are made and yet, many if not most, are without an empirical Figure 1: User interface showing parameter sliderbars that foundation. For example, the standard protocol for creating modify interaction characteristics.

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.

This paper surveys research in robotics in the AAMAS (Au- tonomous Agents and Multi-Agent Systems) community. It argues that the autonomous agents community can, and has, impact on robotics. Moreover, it argues that agents re- searchers should proactively seek to impact the robotics com- munity, to prevent independent re-discovery of known results, and to benefit autonomous agents science. To support these claims, I provide evidence from my own research into multi- robot teams, and from others’.

Market or auction based algorithms offer effective methods for de-centralized task assignment in multi-agent teams. Typically there is an implicit assumption that agents are willing to cooperate and can be trusted to perform assigned tasks. Reciprocal collaboration may not always be a valid assumption. In cases where auctions are used for task allocation, without explicit revenue exchange, incentives are needed to enforce cooperation. An approach to incentive based trust is presented, which enables detection of team members that are not contributing and for dynamic formation of teams.

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.