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.

We investigate the problem of creating a robust, rapidly converging, Distributed Communication Architecture (DCA) for the domain of low bandwidth, single channel Multiagent Systems (MAS) in which agents may drop in and out of communication without prior notification. There are only three capability-based assumptions made by the algorithm: 1) agents can classify a signal's message as either noise, silence, or clarity, 2) agents can classify their own messages, and 3) agents can understand one another to some degree. The final structure allows agents to communicate in a round-robin manner without any centralized or hierarchical control. We evaluate DCA's the convergence rate through four distinct experiments, including both a worst-case scenario that consists of all agents starting simultaneously and a more common-case scenario in which agents offset their starting times. We examine effective throughput as the average number of clearly sent messages in a cycle to determine the amount of information successfully communicated. Lastly, we emulate situations found in problems with moving agents to show that agents who incorporate local observations can improve both their convergence rates and throughput.

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.

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.

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.

In this article, I show the robustness of optimality of exploration ratioagainst the number of agents (agent population)under multiagent learning (MAL) situation in nonstationary environments.Agent population will affect efficiency of agents' learning becauseeach agent's learning causes noisy factors for others.From this point, exploration ratio should be small to make MAL effective.In nonstationary environments, on the other hand, each agent needs explore with enough probability to catch-upchanges of the environments.This means the exploration ratio need to be significantly large.I investigate the relation between the population and the efficiency ofexploration based on a theorem about relations betweenthe exploration ratio and a lower boundary of learning error.Finally, it is shown that the population of the agents does not affectthe optimal exploration ratio under a certain condition.This consequence is confirmed by several experimentsusing population games with various reward functions.

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

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 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.