United Kingdom

We explore the class of problems where a central planner needs to select a subset of agents, each with different quality and cost. The planner wants to maximize its utility while ensuring that the average quality of the selected agents is above a certain threshold. When the agents&#39; quality is known, we formulate our problem as an integer linear program (ILP) and propose a deterministic algorithm, namely \dpss\ that provides an exact solution to our ILP.

AAMAS 2021

A Multi-Arm Bandit Approach To Subset Selection Under Constraints

We explore the class of problems where a central planner needs to select a subset of agents, each with different quality and cost. The planner wants to maximize its utility while ensuring that the average quality of the selected agents is above a certain threshold. When the agents' quality is known, we formulate our problem as an integer linear program (ILP) and propose a deterministic algorithm, namely \dpss\ that provides an exact solution to our ILP.

poster

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Artificial intelligence (AI) planning models play an important role in decision support systems for disaster management e.g. typhoon contingency plan development. However, constructing an AI planning model always requires significant amount of manual effort, which becomes a bottleneck to emergency response in a time-critical situation. In this demonstration, we present a framework of automating a domain model of planning domain definition language from natural language input through deep learning techniques. We implement this framework in a typhoon response system and demonstrate automatic generation of typhoon contingency plan from official typhoon plan documents.

ATPT: Automate Typhoon Contingency Plan Generation from Text

Humans can move their bodies and eyes actively to perceive the state of the environment they are surrounded by. Autonomous robots are needed to learn this ability, so called active perception, to behave as humans do. In this paper, we propose a reinforcement learning algorithm to make the robots have the perceptual ability. In our algorithm, we simultaneously train two agents which control the robot and its sensor on the robot to achieve a task. We conducted experiments on navigation tasks in a 3D environment where useful information for the task achievement is partially occluded. The experimental results show that our algorithm can obtain better perceptual behavior and achieve higher success rates than conventional reinforcement learning algorithms.

Simultaneous Learning of Moving and Active Perceptual Policies for Autonomous Robot

We address the problem of multiagent credit assignment  in a large scale multiagent system. Difference rewards (DRs) are an effective tool to tackle this problem, but their exact computation is known to be challenging even for small number of agents. We propose a scalable method to compute difference rewards based on aggregate information in a multiagent system with large number of agents by exploiting the symmetry present in several practical applications. Empirical evaluation on two multiagent domains  air-traffic control and cooperative navigation, shows better solution quality than previous approaches.

Approximate Difference Rewards for Scalable Multigent Reinforcement Learning

In this paper, we present a novel approach for optimising long-term tactical and strategic decision-making in football (soccer) by encapsulating events in a league environment across a given time frame. We model the teams' objectives for a season and track how these evolve as games unfold to give a fluent objective that can aid in decision-making games. We develop Markov chain Monte Carlo and deep learning-based algorithms that make use of the fluent objectives in order to learn from prior games and other games in the environment and increase the teams’ long-term performance. Simulations of our approach using real-world datasets from 760 matches shows that by using optimised tactics with our fluent objective and prior games, we can on average increase teams mean expected finishing distribution in the league by up to 35.6%.

Optimising Long-Term Outcomes using Real-World Fluent Objectives: An Application to Football

When agents independently learn knowledge, such as ontologies, about their environment, it may be diverse, incorrect or incomplete. This knowledge heterogeneity could lead agents to disagree, thus hindering their cooperation. Existing approaches usually deal with this interaction problem by relating ontologies, without modifying them, or, on the contrary, by focusing on building common knowledge. Here, we consider agents adapting ontologies learned from the environment in order to agree with each other when cooperating. In this scenario, fundamental questions arise: Do they achieve successful interaction? Can this process improve knowledge correctness? Do all agents end up with the same ontology? To answer these questions, we design a two-stage experiment. First, agents learn to take decisions about the environment by classifying objects and the learned classifiers are turned into ontologies. In the second stage, agents interact with each other to agree on the decisions to take and modify their ontologies accordingly. We show that agents indeed reduce interaction failure, most of the time they improve the accuracy of their knowledge about the environment, and they do not necessarily opt for the same ontology.

Knowledge Improvement and Diversity under Interaction-Driven Adaptation of Learned Ontologies

We study the complexity of finding a Walrasian equilibrium in markets where the agents have  k -demand valuations. These valuations are an extension of unit-demand valuations where a bundle's value is the maximum of its  k -subsets' values. For unit-demand agents, where the existence of a Walrasian equilibrium is guaranteed, we show that the problem is in quasi-NC. For $k=2$, we show that it is NP-hard to decide if a Walrasian equilibrium exists even if the valuations are submodular, while for $k=3$ the hardness carries over to budget-additive valuations. In addition, we give a polynomial-time algorithm for markets with 2-demand single-minded valuations, or unit-demand valuations.

Walrasian Equilibria in Markets with Small Demands

Communication improves the efficiency and convergence of multi-agent learning. Existing study of agent communication has been limited on predefined fixed connections. While an attention mechanism exists and is useful for scheduling the communication between agents, it, however, largely ignores the dynamical nature of communication and thus the correlation between agents' connections. In this work, we adopt a normalizing flow to encode correlation between agents interactions. The dynamical communication topology is directly learned by maximizing the agent rewards. In our end-to-end formulation, the communication structure is learned by considering it as a hidden dynamical variable. We realize centralized training of critics and graph reasoning policy, and decentralized execution from local observation and message that are received through the learned dynamical communication topology. Experiments on cooperative navigation in the particle world and adaptive traffic control tasks demonstrate the effectiveness of our method.

Learning Correlated Communication Topology in Multi-Agent Reinforcement learning

Taxi fleets and car aggregation systems are an important component of the urban public transportation system. Taxis and cars in taxi fleets and car aggregation systems (e.g., Uber) are dependent on a large number of self-controlled and profit-driven taxi drivers, which introduces inefficiencies in the system. There are two ways in which taxi fleet performance can be optimized: (i) Operational decision making: improve assignment of taxis/cars to customers, while accounting for future demand; (ii) strategic decision making: optimize operating hours of (taxi and car) drivers. Existing research has primarily focused on the operational decisions in (i) and we focus on the strategic decisions in (ii).

Adaptive Operating Hours for Improved Performance of Taxi Fleets

Graceful degradation is a desirable trait in applications that require coverage with real, failure-prone robots. Thus paper uses methods informed by Reliability Engineering to study the Reliability-Aware Multi-Agent Coverage Path Planning (RA-MCPP) problem. An augmented stochastic framework is applied to evaluate a strategy's probability of mission completion. An augmented stochastic framework is applied to evaluate a strategy's probability of mission completion (PoC) on 3D lattice graph environments. A Genetic Algorithm optimisation approach is then proposed to find RA-MCPP path plans which maximise PoC. It is shown that the GA provides good solutions at reasonable runtimes, complementing previous approaches which focused on global optimality guarantees at the cost of massive computation, especially for medium and large environments.

Reliability-Aware Multi-UAV Coverage Path Planning using a Genetic Algorithm

In service robot applications, planning is often integrated with task allocation.  Linear Temporal Logic (LTL) as an expressive high-level formalism is widely used for task specification, and allows for formalised restrictions on temporal sequences of tasks.   In multiagent planning, a Multi-Objective Markov Decision Process extends the standard model with vector rewards capturing possibly conflicting planning objectives. Such objectives include the success rates of accomplishing individual tasks, and the cost budgets for individual agents. In this paper, we consider the problem of concurrently allocating LTL task sequences to a team of agents and calculating optimal task schedulers simultaneously, satisfying cost and probability thresholds. We reduce this problem to multi-objective scheduler synthesis for a team MDP structure, whose size is linear in the number of agents. Our preliminary experiment demonstrates the scalability of our approach.

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ATPT: Automate Typhoon Contingency Plan Generation from Text

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