United Kingdom

This paper studies cohorting in public transit systems and its usefulness in mitigating disease transmission. The Mumbai suburban railway system is used as a case study.

AAMAS 2021

Cohorting to Isolate Asymptomatic Spreaders: An Agent-Based Simulation Study on the Mumbai Suburban Railway

AAMAS is the largest and most influential conference in the area of agents and multiagent systems, bringing together researchers and practitioners in all areas of agent technology and providing and internationally renowned high-profile forum for publishing and finding out about the latest developments in the field. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).  

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poster

When trains are finished with their transportation tasks during the day, they are moved to a shunting yard where they are routed, parked, cleaned, subject to regular maintenance checks and repaired during the night. The resulting Train Unit Shunting and Servicing problem motivates advanced research in planning and scheduling in general since it integrates several known individually hard problems while incorporating many real-life details. We developed an event-based simulator called TORS (Dutch acronym for Train Shunting and Servicing Simulator), that provides the user with a state and all feasible actions. After an action is picked, TORS calculates the result and the process repeats.   This simulator facilitates research into a realistic application of multi-agent path finding.

TORS: A Train Unit Shunting and Servicing Simulator

This extended abstract summarises the contributions from the journal article Fisher et al. (Autonomous Agents and Multi Agent Systems 35, 1 (2021), 8).

Summarising a Framework for the Certification of Reliable Autonomous Systems (JAAMAS Track)

Large teams of heterogeneous agents have the potential to solve complex multi-task problems that are intractable for a single agent working independently. However, solving complex multi-task problems requires leveraging the relative strengths of the different kinds of agents in the team. We present Stochastic TRAit-based Task Assignment (STRATA), a unified framework that models large teams of heterogeneous agents and performs effective task assignments. Specifically, given information on which traits (capabilities) are required for various tasks, STRATA computes the assignments of agents to tasks such that the trait requirements are achieved. Inspired by prior work in robot swarms and biodiversity, we categorize agents into different species (groups) based on their traits. We model each trait as a continuous variable and differentiate between traits that can and cannot be aggregated from different agents. STRATA is capable of reasoning about both species-level and agent-level variability in traits. We illustrate the necessity and effectiveness of STRATA using detailed numerical simulations and in a capture-the-flag game environment.

STRATA: Unified Framework for Task Assignments in Large Teams of Heterogeneous Agents (JAAMAS Track)

Many real-world scenarios involve teams of agents that have to coordinate their actions to reach a shared goal. We focus on the setting in which a team of agents faces an opponent in a zero-sum, imperfect-information game. Team members can coordinate their strategies before the beginning of the game, but are unable to communicate during the playing phase of the game. This is the case, for example, in Bridge, collusion in poker, and collusion in bidding.

Multi-Agent Coordination in Adversarial Environments through Signal Mediated Strategies

We address the problem of strategyproof (SP) facility location mechanisms on discrete trees. Our main result is a full characterization of onto and SP mechanisms. In particular, we prove that when a single agent significantly affects the outcome, the trajectory of the facility is almost contained in the trajectory of the agent, and both move in the same direction along the common edges. We show tight relations of our characterization to previous results on discrete lines and on continuous trees. We then derive further implications of the main result for infinite discrete lines.

Strategyproof Facility Location Mechanisms on Discrete Trees

In cooperative multi-agent reinforcement learning, a collection of agents learns to interact in a shared environment to achieve a common goal. We propose the use of reward machines (RM) -- Mealy machines used as structured representations of reward functions -- to encode the team's task. The proposed novel interpretation of RMs in the multi-agent setting explicitly encodes required teammate interdependencies, allowing the team-level task to be decomposed into sub-tasks for individual agents. We define such a notion of RM decomposition and present algorithmically verifiable conditions guaranteeing that distributed completion of the sub-tasks leads to team behavior accomplishing the original task. This framework for task decomposition provides a natural approach to decentralized learning: agents may learn to accomplish their sub-tasks while observing only their local state and abstracted representations of their teammates. We accordingly propose a decentralized q-learning algorithm. Furthermore, in the case of undiscounted rewards, we use local value functions to derive lower and upper bounds for the global value function corresponding to the team task. Experimental results in three discrete settings exemplify the effectiveness of the proposed RM decomposition approach, which converges to a successful team policy an order of magnitude faster than a centralized learner and significantly outperforms hierarchical and independent q-learning approaches.

Reward Machines for Cooperative Multi-Agent Reinforcement Learning

Most studies investigating models and algorithms for distributed constraint optimization problems (DCOPs) assume messages arrive instantaneously or within a (short) bounded delay.

Latency-Aware Local Search for Distributed Constraint Optimization

The agile software development life cycle is widely used in industry today due to its highly flexible and iterative processes that facilitate rapid prototyping. There has been recent work in bringing concepts and processes from agile methodologies to agent-oriented software engineering (AOSE). We contribute to this effort by presenting in this paper a novel approach to capturing requirements of agent systems in AOSE using and extending agile concepts. In this paper, we propose to adopt and extend the well-known concept of 
 to facilitate the development of agent systems. We introduce a novel concept, 
, that defines requirements from the perspective of the system. These System Stories are refinements of User Stories and provide more intuitive mappings to agent concepts in the design and implementation. We show how our approach allows better traceability of requirements between stories and the different software development artifacts. We validate our proposal with a feature-based comparison to recent related work, and a preliminary user evaluation based on a drone simulation of a simple search and rescue case study.

User and System Stories: An Agile Approach for Managing Requirements in AOSE

Recent work on promoting cooperation in multi-agent learning has resulted in many methods which successfully promote cooperation at the cost of becoming more vulnerable to exploitation by malicious actors. We show that this is an unavoidable trade-off and propose an objective which balances these concerns, promoting both safety and long-term cooperation. Moreover, the trade-off between safety and cooperation is not severe, and you can receive exponentially large returns through cooperation from a small amount of risk. We study both an exact solution method and propose a method for training policies that targets this objective, Accumulating Risk Capital Through Investing in Cooperation (ARCTIC), and evaluate them in iterated Prisoner's Dilemma and Stag Hunt.

Accumulating Risk Capital Through Investing in Cooperation

Prediction markets are powerful tools to elicit and aggregate beliefs from strategic agents. However, in current prediction markets, agents may exhaust the social welfare by competing to be the first to update the market. We initiate the study of the trade-off between how quickly information is aggregated by the market, and how much this information costs. We design markets to aggregate timely information from strategic agents to maximize social welfare. To this end, the market must incentivize agents to invest the correct amount of effort to acquire information: quickly enough to be useful, but not faster (and more expensively) than necessary. The market also must ensure that agents report their information truthfully and on time.  We consider two settings: in the first, information is only valuable before a deadline; in the second, the value of information decreases as time passes.  We use both theorems and simulations to demonstrate the mechanisms.

Cohorting to Isolate Asymptomatic Spreaders: An Agent-Based Simulation Study on the Mumbai Suburban Railway

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