An Agent-Based Simulation for Investigating the Impact of Stereotypes on Task-Oriented Group Formation

In this paper, we introduce an agent-based simulation for investigating the impact of social factors on the formation and evolution of task-oriented groups. Task-oriented groups are created explicitly to perform a task, and all members derive benefits from task completion. However, even in cases when all group members act in a way that is locally optimal for task completion, social forces that have mild effects on choice of associates can have a measurable impact on task completion performance. In this paper, we show how our simulation can be used to model the impact of stereotypes on group formation. In our simulation, stereotypes are based on observable features, learned from prior experience, and only affect an agent\u27s link formation preferences. Even without assuming stereotypes affect the agents\u27 willingness or ability to complete tasks, the long-term modifications that stereotypes have on the agents\u27 social network impair the agents\u27 ability to form groups with sufficient diversity of skills, as compared to agents who form links randomly. An interesting finding is that this effect holds even in cases where stereotype preference and skill existence are completely uncorrelated. © 2011 Springer-Verlag Berlin Heidelberg

Identifying Influential Agents In Social Systems

This dissertation addresses the problem of influence maximization in social networks. In- fluence maximization is applicable to many types of real-world problems, including modeling contagion, technology adoption, and viral marketing. Here we examine an advertisement domain in which the overarching goal is to find the influential nodes in a social network, based on the network structure and the interactions, as targets of advertisement. The assumption is that advertisement budget limits prevent us from sending the advertisement to everybody in the network. Therefore, a wise selection of the people can be beneficial in increasing the product adoption. To model these social systems, agent-based modeling, a powerful tool for the study of phenomena that are difficult to observe within the confines of the laboratory, is used. To analyze marketing scenarios, this dissertation proposes a new method for propagating information through a social system and demonstrates how it can be used to develop a product advertisement strategy in a simulated market. We consider the desire of agents toward purchasing an item as a random variable and solve the influence maximization problem in steady state using an optimization method to assign the advertisement of available products to appropriate messenger agents. Our market simulation 1) accounts for the effects of group membership on agent attitudes 2) has a network structure that is similar to realistic human systems 3) models inter-product preference correlations that can be learned from market data. The results on synthetic data show that this method is significantly better than network analysis methods based on centrality measures. The optimized influence maximization (OIM) described above, has some limitations. For instance, it relies on a global estimation of the interaction among agents in the network, rendering it incapable of handling large networks. Although OIM is capable of finding the influential nodes in the social network in an optimized way and targeting them for advertising, in large networks, performing the matrix operations required to find the optimized solution is intractable. To overcome this limitation, we then propose a hierarchical influence maximization (HIM) iii algorithm for scaling influence maximization to larger networks. In the hierarchical method the network is partitioned into multiple smaller networks that can be solved exactly with optimization techniques, assuming a generalized IC model, to identify a candidate set of seed nodes. The candidate nodes are used to create a distance-preserving abstract version of the network that maintains an aggregate influence model between partitions. The budget limitation for the advertising dictates the algorithm’s stopping point. On synthetic datasets, we show that our method comes close to the optimal node selection, at substantially lower runtime costs. We present results from applying the HIM algorithm to real-world datasets collected from social media sites with large numbers of users (Epinions, SlashDot, and WikiVote) and compare it with two benchmarks, PMIA and DegreeDiscount, to examine the scalability and performance. Our experimental results reveal that HIM scales to larger networks but is outperformed by degreebased algorithms in highly-connected networks. However, HIM performs well in modular networks where the communities are clearly separable with small number of cross-community edges. This finding suggests that for practical applications it is useful to account for network properties when selecting an influence maximization method

Autonomous Agents Modelling Other Agents: A Comprehensive Survey and Open Problems

Much research in artificial intelligence is concerned with the development of autonomous agents that can interact effectively with other agents. An important aspect of such agents is the ability to reason about the behaviours of other agents, by constructing models which make predictions about various properties of interest (such as actions, goals, beliefs) of the modelled agents. A variety of modelling approaches now exist which vary widely in their methodology and underlying assumptions, catering to the needs of the different sub-communities within which they were developed and reflecting the different practical uses for which they are intended. The purpose of the present article is to provide a comprehensive survey of the salient modelling methods which can be found in the literature. The article concludes with a discussion of open problems which may form the basis for fruitful future research.Comment: Final manuscript (46 pages), published in Artificial Intelligence Journal. The arXiv version also contains a table of contents after the abstract, but is otherwise identical to the AIJ version. Keywords: autonomous agents, multiagent systems, modelling other agents, opponent modellin

Learning to coordinate in sparse asymmetric multiagent systems

Multiagent learning offers a rich framework to address challenging real-world problems such as remote exploration and healthcare coordination, which require autonomous agents to express elaborate interactions. To be effective in such systems, agents must collectively reason about and pursue high-level, long-term, and possibly nebulous objectives while adapting their strategy to changing environments, inter-agent relationships, and team dynamics. This work introduces six contributions that address this multifaceted problem through the lens of two distinct perspectives: reward structures for high-level objectives that allow agents to consider behaviors before pursuing them, and diversity structures that incentivize asymmetric agents (agents with distinct capabilities and egocentric objectives) to discover complementary specializations required for robust teamwork. The first contribution, Asymmetric D++, distills sparse team feedback into dense informative rewards by encouraging agents to create asymmetric counterfactuals based on their likelihood to cooperate. The second contribution introduces an uncertainty-aware reward approximation that enables the application of Asymmetric D++ for exploration and learning in sparse reward settings. The third contribution, Behavior Refinement, presents a hierarchical framework that shifts focus from optimizing a single behavior to learning a repertoire of diverse behaviors required to complete variegated tasks. Behavior Refinement allows systematic exploration of the policy space via a combination of diversity search and team-objective maximization. The fourth contribution introduces the Island Model, a computational framework that builds on Behavior Refinement for informed behavior space exploration and team balancing for asymmetric agents. The final two contributions, expand upon the Island Model to develop an asynchronous learning framework that allows asymmetric agents to explore diverse environment-agnostic inter-agent relationships to balance multiple potentially conflicting objectives. The amalgamation of this work facilitates asymmetric agents to learn diverse specializations, express complex trade-offs, and discover robust inter-agent relationships required to solve challenging coordination problems. Additionally, the techniques introduced in this work aid in investigating the rich tapestry of agent synergies that evolve in response to changes in the environment and team objectives.Keywords: Multiagent Reinforcement Learning, Multiagent Coordination, Asymmetric Multiagent Systems, Multiagent Evolutionary Learnin

SPICA:revealing the hearts of galaxies and forming planetary systems : approach and US contributions

How did the diversity of galaxies we see in the modern Universe come to be? When and where did stars within them forge the heavy elements that give rise to the complex chemistry of life? How do planetary systems, the Universe's home for life, emerge from interstellar material? Answering these questions requires techniques that penetrate dust to reveal the detailed contents and processes in obscured regions. The ESA-JAXA Space Infrared Telescope for Cosmology and Astrophysics (SPICA) mission is designed for this, with a focus on sensitive spectroscopy in the 12 to 230 micron range. SPICA offers massive sensitivity improvements with its 2.5-meter primary mirror actively cooled to below 8 K. SPICA one of 3 candidates for the ESA's Cosmic Visions M5 mission, and JAXA has is committed to their portion of the collaboration. ESA will provide the silicon-carbide telescope, science instrument assembly, satellite integration and testing, and the spacecraft bus. JAXA will provide the passive and active cooling system (supporting the

The Apertif Surveys:The First Six Months

Apertif is a new phased-array feed for the Westerbork Synthesis Radio Telescope (WSRT), greatly increasing its field of view and turning it into a natural survey instrument. In July 2019, the Apertif legacy surveys commenced; these are a time-domain survey and a two-tiered imaging survey, with a shallow and medium-deep component. The time-domain survey searches for new (millisecond) pulsars and fast radio bursts (FRBs). The imaging surveys provide neutral hydrogen (HI), radio continuum and polarization data products. With a bandwidth of 300 MHz, Apertif can detect HI out to a redshift of 0.26. The key science goals to be accomplished by Apertif include localization of FRBs (including real-time public alerts), the role of environment and interaction on galaxy properties and gas removal, finding the smallest galaxies, connecting cold gas to AGN, understanding the faint radio population, and studying magnetic fields in galaxies. After a proprietary period, survey data products will be publicly available through the Apertif Long Term Archive (ALTA, https://alta.astron.nl). I will review the progress of the surveys and present the first results from the Apertif surveys, including highlighting the currently available public data