26,998 research outputs found
Time-Independent Planning for Multiple Moving Agents
Typical Multi-agent Path Finding (MAPF) solvers assume that agents move
synchronously, thus neglecting the reality gap in timing assumptions, e.g.,
delays caused by an imperfect execution of asynchronous moves. So far, two
policies enforce a robust execution of MAPF plans taken as input: either by
forcing agents to synchronize or by executing plans while preserving temporal
dependencies. This paper proposes an alternative approach, called
time-independent planning, which is both online and distributed. We represent
reality as a transition system that changes configurations according to atomic
actions of agents, and use it to generate a time-independent schedule.
Empirical results in a simulated environment with stochastic delays of agents'
moves support the validity of our proposal.Comment: 10 pages, 5 figures, to be presented at AAAI-21, Feb 2021, Virtual
Conferenc
Fault-Tolerant Offline Multi-Agent Path Planning
We study a novel graph path planning problem for multiple agents that may
crash at runtime, and block part of the workspace. In our setting, agents can
detect neighboring crashed agents, and change followed paths at runtime. The
objective is then to prepare a set of paths and switching rules for each agent,
ensuring that all correct agents reach their destinations without collisions or
deadlocks, despite unforeseen crashes of other agents. Such planning is
attractive to build reliable multi-robot systems. We present problem
formalization, theoretical analysis such as computational complexities, and how
to solve this offline planning problem.Comment: to be presented at AAAI-2
Using a Cognitive Architecture for Opponent Target Prediction
One of the most important aspects of a compelling game AI is that it anticipates the playerâs actions and responds to them in a convincing manner. The first step towards doing this is to understand what the player is doing and predict their possible future actions. In this paper we show an approach where the AI system focusses on testing hypotheses made about the playerâs actions using an implementation of a cognitive architecture inspired by the simulation theory of mind. The application used in this paper is to predict the target that the player is heading towards, in an RTS-style game. We improve the prediction accuracy and reduce the number of hypotheses needed by using path planning and path clustering
Lifelong Multi-Agent Path Finding in Large-Scale Warehouses
Multi-Agent Path Finding (MAPF) is the problem of moving a team of agents to
their goal locations without collisions. In this paper, we study the lifelong
variant of MAPF, where agents are constantly engaged with new goal locations,
such as in large-scale automated warehouses. We propose a new framework
Rolling-Horizon Collision Resolution (RHCR) for solving lifelong MAPF by
decomposing the problem into a sequence of Windowed MAPF instances, where a
Windowed MAPF solver resolves collisions among the paths of the agents only
within a bounded time horizon and ignores collisions beyond it. RHCR is
particularly well suited to generating pliable plans that adapt to continually
arriving new goal locations. We empirically evaluate RHCR with a variety of
MAPF solvers and show that it can produce high-quality solutions for up to
1,000 agents (= 38.9\% of the empty cells on the map) for simulated warehouse
instances, significantly outperforming existing work.Comment: Published at AAAI 202
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