Language evolution might have preferred certain prior social configurations over others. Experiments conducted with models of different social structures (varying subgroup interactions and the role of a dominant interlocutor) suggest that having isolated agent groups rather than an interconnected agent is more advantageous for the emergence of a social communication system. Distinctive groups that are closely connected by communication yield systems less like natural language than fully isolated groups inhabiting the same world. Furthermore, the addition of a dominant male who is asymmetrically favoured as a hearer, and equally likely to be a speaker has no positive influence on the disjoint groups.

To deal with the prohibitive complexity of calculating policies in Decentralized MDPs, researchers have proposed models that exploit structured agent interactions. Settings where most agent actions are independent except for few actions that affect the transitions and/or rewards of other agents can be modeled using Event-Driven Interactions with Complex Rewards (EDI-CR). Finding the optimal joint policy can be formulated as an optimization problem. However, existing formulations are too verbose and/or lack optimality guarantees. We propose a compact Mixed Integer Linear Program formulation of EDI-CR instances. The key insight is that most action sequences of a group of agents have the same effect on a given agent. This allows us to treat these sequences similarly and use fewer variables. Experiments show that our formulation is more compact and leads to faster solution times and better solutions than existing formulations.

A complex system is made up of many components with many interactions. So the design of systems such as simulation systems, cooperative systems or assistance systems includes a very accurate modelling of interactional and communicational levels. The agent-based approach provides an adapted abstraction level for this problem. After having studied the organizational context and communicative capacities of agentbased systems, to simulate the reorganization of a flexible manufacturing, to regulate an urban transport system, and to simulate an epidemic detection system, our thoughts on the interactional level were inspired by human-machine interface models, especially those in "cognitive engineering". To provide a general framework for agent-based complex systems modelling, we then proposed a scale of four behaviours that agents may adopt in their complex systems (reactive, routine, cognitive, and collective). To complete the description of multi-level agent models, which is the focus of this paper, we illustrate our modelling and discuss our ongoing work on each level.

We describe a cognitive model of a collaborative agent that can serve as the basis for automated systems that must collaborate with other agents, including humans, to solve problems. This model builds on standard approaches to cognitive architecture and intelligent agency, as well as formal models of speech acts, joint intention, and intention recognition. The model is nonetheless intended for practical use in the development of collaborative systems.

We consider the problem of dynamic self-reconfiguration in a modular self-reconfigurable robot (MSR). Previous MSR self-reconfiguration approaches search for new configurations only within the modules of the MSR that needs reconfiguration. In contrast, we describe a technique where an MSR that needs to reconfigure communicates with other MSRs in its vicinity to determine if modules can be shared from other MSRs, and then determines the best possible configuration among the combined set of modules. We model the MSR self-reconfiguration problem as a coalition structure generation problem within a coalition game theoretic framework. We formulate the coalition structure generation problem as a planning problem in the presence of uncertainty and propose an MDP-based algorithm to solve it. We have implemented our algorithm within an MSR called ModRED that is simulated on the Webots simulation platform. Our results show that using our self-reconfiguration algorithm, when an MSR needs to reconfigure, a new configuration that is within 5-7% of the globally optimal configuration can be determined. We have also shown that our algorithm performs comparably with another existing algorithm for determining optimal coalition structure.

In many multiagent scenarios, groups of participants (known as coalitions) may attempt to cooperate, seeking to increase the benefits realized by the members. Depending on the scenario, such cooperation may be benign, or may be unwelcome or even forbidden (often called collusion). Coalitions can present a problem for many multiagent systems, potentially undermining the intended operation of systems. In this paper, we present a technique for detecting the presence of coalitions (malicious or otherwise), and identifying their members. Our technique employs clustering in benefit space, a high-dimensional feature space reflecting the benefit flowing between agents, in order to identify groups of agents who are similar in terms of the agents they are favoring. A statistical approach is then used to characterize candidate clusters, identifying as coalitions those groups that favor their own members to a much greater degree than the general population. We believe that our approach is applicable to a wide range of domains. Here, we demonstrate its effectiveness within a simulated marketplace making use of a trust and reputation system to cope with dishonest sellers. Many trust and reputation proposals readily acknowledge their ineffectiveness in the face of collusion, providing one example of the importance of the problem. While certain aspects of coalitions have received significant attention (e.g., formation, stability, etc.), relatively little research has focused on the problem of coalition identification. We believe our research represents an important step towards addressing the challenges posed by coalitions.

HAPTIC is individuals - agents or humans - within them to establish grounded in game theory and probabilistic modeling. It has successful relationships with their partners. In Supply been proved that HAPTIC agents learn other agents' behaviors Chain Management (SCM), establishing trust improves the reliably using direct observations. One shortcoming of chances of a successful supply chain relationship, and increases HAPTIC is that it does not support reported observations.

The conventional model of coalition formation considers every possible subset of agents as a potential coalition. However, in many real-world applications, there are inherent constraints on feasible coalitions: for instance, certain agents may be prohibited from being in the same coalition, or the coalition structure may be required to consist of coalitions of the same size. In this paper, we present the first systematic study of constrained coalition formation (CCF). We propose a general framework for this problem, and identify an important class of CCF settings, where the constraints specify which groups of agents should/should not work together. We describe a procedure that transforms such constraints into a structured input that allows coalition formation algorithms to identify, without any redundant computations, all the feasible coalitions. We then use this procedure to develop an algorithm for generating an optimal (welfare-maximizing) constrained coalition structure, and show that it outperforms existing state-of-the-art approaches by several orders of magnitude.

Our approach Multi-agent task allocation is an important and challenging yields significant reductions in both run-time and communication, problem, which involves deciding how to assign a set thereby increasing real-world applicability. of agents to a set of tasks, both of which may change over In more detail, in this paper we advance the state-ofthe-art time (i.e., it is a dynamic environment). Moreover, it is often in the following ways: first, we present a novel, necessary for heterogeneous agents to form teams (known as online domain pruning algorithm specifically tailored to coalitions) to complete certain tasks in the environment. In dynamic task allocation environments to reduce the number coalitions, agents can often complete tasks more efficiently of potential solutions that need to be considered.

In many real-life optimization problems involving multiple agents, the rewards are not necessarily known exactly in advance, but rather depend on sources of exogenous uncertainty. For instance, delivery companies might have to coordinate to choose who should serve which foreseen customer, under uncertainty in the locations of the customers. The framework of Distributed Constraint Optimization under Stochastic Uncertainty was proposed to model such problems; in this paper, we generalize this formalism by introducing the concept of evaluation functions that model various optimization criteria. We take the example of three such evaluation functions, expectation , consensus , and robustness , and we adapt and generalize two previous algorithms accordingly. Our experimental results on a class of Vehicle Routing Problems show that incomplete algorithms are not only cheaper than complete ones (in terms of simulated time , Non-Concurrent Constraint Checks , and information exchange) , but they are also often able to find the optimal solution. We also show that exchanging more information about the dependencies of their respective cost functions on the sources of uncertainty can help the agents discover higher-quality solutions.