United Kingdom

In swarm robotics, research mainly follows a theoretical approach that considers robot systems in the abstract, where the complexity and capabilities of the underlying model are often reduced to their minimum. One general and well-investigated model is &amp;#248;blot, where the robots are silent, anonymous, and oblivious. In this work, we introduce MOBLOT, a model that extends &amp;#248;blot to address a larger spectrum of cases. MOBLOT stands for molecular oblivious robots: like atoms combine themselves to form molecules, in MOBLOT simple robots can move to form more complex computational units, having an extent and different capabilities with respect to robots; like molecules combine themselves to form the matter, in MOBLOT the complex structures can exploit their own capabilities to arrange themselves to form any shape defining an acceptable final structure.   In MOBLOT, we formally define the Matter Formation (MF) problem and, as a preliminary general result, we provide a necessary condition for its solvability which relies on symmetricity.  Informally, the symmetricity of a configuration measures the amount of symmetries of the robots&#39; disposal.  We actually show how dealing with molecules can resolve in some cases the symmetry breaking issue where &amp;#248;blot cannot. Finally, we provide a case study for MOBLOT, that is, a representative MF problem along with a resolution distributed algorithm.

AAMAS 2021

MOBLOT: Molecular Oblivious Robots

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In swarm robotics, research mainly follows a theoretical approach that considers robot systems in the abstract, where the complexity and capabilities of the underlying model are often reduced to their minimum. One general and well-investigated model is &#248;blot, where the robots are silent, anonymous, and oblivious. In this work, we introduce MOBLOT, a model that extends &#248;blot to address a larger spectrum of cases. MOBLOT stands for molecular oblivious robots: like atoms combine themselves to form molecules, in MOBLOT simple robots can move to form more complex computational units, having an extent and different capabilities with respect to robots; like molecules combine themselves to form the matter, in MOBLOT the complex structures can exploit their own capabilities to arrange themselves to form any shape defining an acceptable final structure.   In MOBLOT, we formally define the Matter Formation (MF) problem and, as a preliminary general result, we provide a necessary condition for its solvability which relies on symmetricity.  Informally, the symmetricity of a configuration measures the amount of symmetries of the robots' disposal.  We actually show how dealing with molecules can resolve in some cases the symmetry breaking issue where &#248;blot cannot. Finally, we provide a case study for MOBLOT, that is, a representative MF problem along with a resolution distributed algorithm.

technical paper

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Partially Cooperative Multi-Agent Periodic Indivisible Resource Allocation

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Rank Aggregation by Dissatisfaction Minimisation in the Unavailable Candidate Model

Van Zuylen et al. introduced the notion of a popular ranking in a voting context, where each voter submits a strictly-ordered list of all candidates. A popular ranking pi of the candidates is at least as good as any other ranking sigma in the following sense: if we compare pi to sigma, at least half of all voters will always weakly prefer pi. Whether a voter prefers one ranking to another is calculated based on the Kendall distance.

On Weakly and Strongly Popular Rankings

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Individuals in a social network may form their views as a result of the influence exerted by their connections. In elections, for example, while they might initially support one candidate, social influence may lead them to support another. Here, we investigate whether a recently proposed metric, influence gap, designed to measure the effect of social influence in voting on social networks, is able to predict the outcome of a vote on networks exhibiting community structure, i.e., made of highly interconnected components, and therefore more resembling of real-world interaction. To encode communities, we extend the classical model of caveman graphs to a richer graph family that displays levels of homophily, i.e., where connections and opinions are highly intertwined. We show that, across these graphs, there are important cases when the influence gap correlation is a weak predictor due to communities, and a simpler metric, counting the initial partisan majority, provides a more accurate prediction overall. Using regression models, we further demonstrate that the influence gap combined with the more successful metrics does increase their predictive power for some levels of homophily.

Predicting Voting Outcomes in Presence of Communities

The Possible Winner (PW) problem, a fundamental algorithmic problem in computational social choice, concerns elections where voters express only partial preferences between candidates. A sequence of investigations led to a complete classification of the complexity of this problem for all pure positional scoring rules: the PW problem is in P for the plurality and veto rules, and NP-complete for all other such rules. The PW problem has also been studied on classes of restricted partial orders, such as partitioned partial orders and truncated partial orders; one of the findings is that there are positional scoring rules for which the complexity of the PW problem drops from NP-complete to P on such restricted partial orders. Here, we investigate the PW problem on partial chains, i.e., partial orders that consist of a total order on a subset of their domains. Such orders arise naturally in a variety of settings, including rankings of movies or restaurants. We classify the complexity of the PW problem on partial chains by establishing that, perhaps surprisingly, this restriction does not change the complexity of the problem. Specifically, we show that the PW problem on partial chains is NP-complete for all pure positional scoring rules other than the plurality rule and the veto rule, while, of course, for the latter two rules this problem remains in P.

Classifying the Complexity of the Possible Winner Problem on Partial Chains

We study settings where a set of identical, reusable resources must be allocated in an online fashion to arriving agents. Each arriving agent is patient and willing to wait for some period of time to be matched. When matched, each agent occupies a resource for a certain amount of time, and then releases it, gaining some utility from having done so. The goal of the system designer is to maximize overall utility given some prior knowledge of the distribution of arriving agents. We are particularly interested in settings where demand for the resources far outstrips supply, as is typical in the provision of social services, for example homelessness resources. We formulate this problem as online bipartite matching with reusable resources and patient agents. We develop new, efficient nonmyopic algorithms for this class of problems, and compare their performance with that of greedy algorithms in a variety of simulated settings, as well as in a setting calibrated to real-world data on household demand for homelessness services. We find substantial overall welfare benefits to using our nonmyopic algorithms, particularly in more extreme settings -- those where agents are unwilling or unable to wait for resources, and where the ratio of resource demand to supply is particularly high.

Efficient Nonmyopic Online Allocation of Scarce Reusable Resources

Multi-agent reinforcement learning (MARL) requires coordination to efficiently solve certain tasks. Fully centralized control is often infeasible in such domains due to the size of joint action spaces. Coordination graph based formalization allows reasoning about the joint action based on the structure of interactions. However,they often require domain expertise in their design. This paper introduces the deep implicit coordination graph (DICG) architecture for such scenarios. DICG consists of a module for inferring the dynamic coordination graph structure which is then used by a graph neural network based module to learn to implicitly reason about the joint actions or values. DICG allows learning the tradeoff between full centralization and decentralization via standard actor-critic methods to significantly improve coordination for domains with large number of agents. We apply DICG to both centralized-training-centralized-execution and centralized-training-decentralized-execution regimes. We demonstrate that DICG solves the relative over generalization pathology in predatory-prey tasks as well as outperforms various MARL baselines on the challenging StarCraft II Multi-agent Challenge (SMAC) and traffic junction environments.

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Traffic congestion is a major challenge in modern urban settings. The industry-wide development of autonomous and automated vehicles (AVs) motivates the question of how can AVs contribute to congestion reduction. Past research has shown that in small scale mixed traffic scenarios with both AVs and human-driven vehicles, a small fraction of AVs executing a controlled multiagent driving policy can mitigate congestion. In this paper, we scale up existing approaches and develop new multiagent driving policies for AVs in scenarios with greater complexity. We start by showing that a congestion metric used by past research is manipulable in open road network scenarios where vehicles dynamically join and leave the road. We then propose using a different metric that is robust to manipulation and reflects open network traffic efficiency. Next, we propose a modular transfer learning approach and use it to scale up the multiagent driving policy to a realistic simulated scenario that is an order of magnitude larger than past scenarios (hundreds rather than tens of vehicles). Our experimental study shows that the resulting policy improves traffic efficiency over human-driven traffic in a large open network, where existing approaches fail to do so. Another key advantage of our modular transfer learning approach is that it avoids collecting samples from entire network, which saves up to 80% of training and data collection time in our experiments. Finally, we show for the first time a distributed multiagent policy that improves congestion over human-driven traffic. The distributed approach is more realistic as it does not require adding communication infrastructure, it relies on existing sensing capabilities, and its complexity scales linearly with the number of vehicles.

MOBLOT: Molecular Oblivious Robots

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MOBLOT: Molecular Oblivious Robots

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Next from AAMAS 2021

Partially Cooperative Multi-Agent Periodic Indivisible Resource Allocation

Similar lecture

Action Priors for Large Action Spaces in Robotics

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PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads