Multi-agent path planning (MAPP) is the problem of planning collision-free trajectories from start to goal locations for a team of agents. This work explores a relatively unexplored setting of MAPP where streams of agents have to go through the starts and goals with high throughput. We tackle this problem by formulating a new variant of MAPP called periodic MAPP in which the timing of agent appearances is periodic. The objective with periodic MAPP is to find a periodic plan, a set of collision-free trajectories that the agent streams can use repeatedly over periods, with periods that are as small as possible. To meet this objective, we propose a solution method that is based on constraint relaxation and optimization. We show that the periodic plans once found can be used for a more practical case in which agents in a stream can appear at random times. We confirm the effectiveness of our method compared with baseline methods in terms of throughput in several scenarios that abstract autonomous intersection management tasks.

Periodic Multi-Agent Path Planning


Multi-agent path planning (MAPP) in continuous spaces is a challenging problem with significant practical importance. One promising approach could first construct graphs approximating the spaces called roadmaps, then apply multi-agent pathfinding (MAPF) algorithms on those roadmaps to derive a set of conflict-free trajectories. While conventional studies have just utilized roadmap construction methods developed for single-agent planning, it remains largely unexplored what can serve as effective roadmaps for multiple agents and how we can construct them. To this end, we propose a novel concept of graph representations of the space named cooperative timed roadmaps (CTRMs). CTRMs enable each agent to focus on their important locations around potential solution paths, in a way to consider the behavior of other agents to avoid inter-agent collisions (i.e., "cooperative"), while being augmented in time direction to make it easy to derive a "timed" solution path. To construct CTRMs, we develop a machine learning approach that learns a generative model from a collection of relevant problem instances and plausible solutions and uses the learned model to sample the vertices of CTRMs for new, previously-unseen problem instances. Our empirical evaluation revealed that the use of CTRMs significantly reduced the planning effort with acceptable overheads while maintaining its success rate and solution quality comparable to conventional roadmap construction approaches.

CTRMs: Learning to Construct Cooperative Timed Roadmaps for Multi-agent Path Planning in Continuous Spaces

This paper addresses the problem of decentralized learning
to achieve a high-performance global model by asking a
group of clients to share local models pre-trained with
their own data resources. We are particularly interested in
a specific case where both the client model architectures
and data distributions are diverse, which makes it
nontrivial to adopt conventional approaches such as
Federated Learning and network co-distillation. To this
end, we propose a new decentralized learning method called
Decentralized Learning via Adaptive Distillation (DLAD).
Given a collection of client models and a large number of
unlabeled distillation samples, the proposed DLAD 1)
aggregates the outputs of the client models while
adaptively emphasizing those with higher confidence in
given distillation samples and 2) trains the global model
to imitate the aggregated outputs. Our extensive
experimental evaluation on multiple public datasets (MNIST,
CIFAR-10, and CINIC-10) demonstrates the effectiveness of
the proposed method.

Adaptive Distillation for Decentralized Learning from Heterogeneous Clients

ROB: Motion and Path Planning / Learning & Optimization for ROB

poster

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AAAI 2023

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**Session Chair:** Roberta Calegari

Oral Session 1A3-1

technical paper

AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

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AAMAS 2022

AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

PS T1.13: Neural Networks for Pattern Recognition

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
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Ryo Yonetani

3

1

SHORT BIO

Presentations

Periodic Multi-Agent Path Planning

CTRMs: Learning to Construct Cooperative Timed Roadmaps for Multi-agent Path Planning in Continuous Spaces

Adaptive Distillation for Decentralized Learning from Heterogeneous Clients

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