We explore the emergent behavior of game theoretic algorithms in a highly dynamic applied setting in which the optimal goal for the agents is constantly changing. Our focus is on a variant of the traditional predator-prey problem entitled Defender. Consisting of multiple predators and multiple prey, Defender shares similarities with rugby, soccer, and football, in addition to current problems in the field of Multiagent Systems (MAS). Observations, communications, and knowledge about the world-state are designed to be information-sparse, modeling real-world uncertainty. We propose a solution to Defender by means of the well-known multiagent learning algorithm fictitious play, and compare it with rational learning, regret matching, minimax regret, and a simple greedy strategy. We provide the modifications required to build these agents and state the implications of their application of them to our problem. We show fictitious play's performance to be superior at evenly assigning predators to prey in spite of it being an incomplete and imperfect information game that is continually changing its dimension and payoff. Interestingly, its performance is attributed to a synthesis of fictitious play, partial observability, and an anti-coordination game which reinforces the payoff of actions that were previously taken.

The problem of coordination in cooperative multiagent systems has been widely studied in the literature. In practical complex environments, the interactions among agents are usually regulated by their underlying network topology, which, however, has not been taken into consideration in previous work. To this end, we firstly investigate the multiagent coordination problems in cooperative environments under the networked social learning framework focusing on two representative topologies: the small-world and the scale-free network. We consider a population of agents where each agent interacts with another agent randomly chosen from its neighborhood in each round. Each agent learns its policy through repeated interactions with its neighbors via social learning. It is not clear a priori if all agents can learn a consistent optimal coordination policy and what kind of impact different topology parameters could have on the learning performance of agents. We distinguish two types of learners: individual action learner and joint action learner. The learning performances of both learners are evaluated extensively in different cooperative games, and the influence of different factors on the learning performance of agents is investigated and analyzed as well.

This workshop focuses on models and algorithms for multiagent interaction without priorcoordination.

We present several epistemic views of ideation in scientific discovery that we have investigated: conceptual classification, abductive explanation, conceptual modeling, analogical reasoning, and visual reasoning. We then describe an experiment in computational ideation through model construction, evaluation and revision. We describe an interactive tool called MILA–S that enables construction of conceptual models of ecological phenomena, agent-based simulations of the conceptual model, and revision of the conceptual model based on the results of the simulation.   The key feature of MILA–S is that it automatically generates the simulations from the conceptual model. We report on a pilot study with 50 middle school science students who used MILA–S to discover causal explanations for an ecological phenomenon. Initial results from the study indicate that use of MILA–S had a significant impact both on the process of model construction and the nature of the constructed models.  We posit that MILA–S may enable scientists to construct, evaluate and revise conceptual models of ecological phenomena.

In this research-in-progress paper we present a new real world domain for studying the aggregation of different opinions: early stage architectural design of buildings. This is an important real world application, not only because building design and construction is one of the world's largest industries measured by global expenditures, but also because the early stage design decision making has a significant impact on the energy consumption of buildings. We present a mapping between the domain of architecture and engineering research and that of the agent models present in the literature. We study the importance of forming diverse teams when aggregating the opinions of different agents for architectural design, and also the effect of having agents optimizing for different factors of a multi-objective optimization design problem. We find that a diverse team of agents is able to provide a higher number of top ranked solutions for the early stage designer to choose from. Finally, we present the next steps for a deeper exploration of our questions.

It is well known that human preferences in decisions underrisk do not always complies with expected utility theory(EUT). In fact, there are several effects that are inconsistent with basic tenets of EUT. Alternative theories have been proposed and perhaps the most well studied is Prospect Theory(PT). Recent work showed experimental results that support the idea that financial professionals may behave according toPT and violate EUT. Meanwhile, some argue that economy needs agent-based modeling, because it may be a better way to help guide financial policies than mathematical models.If financial professional behave according to PT in markets,then agent-based modeling needs PT based agents. Our ideais creating trading agents based on PT to simulate a market.However, the creation of an artificial agent based on PT as originally proposed is very hard and limited to two outcome prospects. We propose an agent model based on an extension of PT called Smooth Prospect Theory (SPT). We used this model to create agents to populate an artificial market withSPT and EUT agents. It was used to predict real market behavior for short periods. SPT agents provided more accurate predictions in crisis periods than EUT agents.

List of organizers of the Multiagent Interaction without Prior Coordination AAAI-14 Workshop.

Localization, personalization, activity recognition, and cognitive assistance are key issues in research on smart homes for cognitively impaired people. Most of the current solutions rely on the presence of solely one person in the residence. To actively consider the interaction of the smart home inhabitant with their caregivers, nurses, doctors and people sharing their home, this paper proposes a multi-agent approach to transparently locate, identify, and ease the collaboration between distributed personalization and assistance services. Based on Bayesian filtering localization using anonymous sensors, the multiperson localization process provides information on each occupant presence, incoming and outgoing. This information is then used for personalization and assistance.

The continuous progress on hardware and software is allowing the appearance of compact and relatively inexpensive autonomous vehicles embedded with multiple sensors (inertial systems, cameras, radars, environmental monitoring sensors), high-bandwidth wireless communication and powerful computational resources. While previously limited to military applications, nowadays the use of cooperating vehicles for autonomous monitoring and large environment, even for civilian applications, is becoming a reality. Although robotics research has obtained tremendous achievements with single vehicles, the trend of adopting multiple vehicles that cooperate to achieve a common goal is still very challenging and open problem. In particular, an area that has attracted considerable attention for its practical relevance is the problem of environmental partitioning problem and coverage control whose objective is to partition an area of interest into subregions each monitored by a different robot trying to optimize some global cost function that measures the quality of service provided by the monitoring robots. The "centering and partitioning" algorithm originally proposed by Lloyd [1] and elegantly reviewed in the survey [2] is a classic approach to environmental partitioning problems and coverage control problems.

The psychological state of flow has been linked to optimizing human performance. A key condition of flow emergence is a match between the human abilities and complexity of the task. We propose a simple computational model of flow for Artificial Intelligence (AI) agents. The model factors the standard agent-environment state into a self-reflective set of the agent's abilities and a socially learned set of the environmental complexity. Maximizing the flow serves as a meta control for the agent. We show how to apply the meta-control policy to a broad class of AI control policies and illustrate our approach with a specific implementation. Results in a synthetic testbed are promising and open interesting directions for future work.