scholarly journals Implicitly Coordinated Multi-Agent Path Finding under Destination Uncertainty: Success Guarantees and Computational Complexity (Extended Abstract)

2019 ◽  
Author(s):  
Bernhard Nebel ◽  
Thomas Bolander ◽  
Thorsten Engesser ◽  
Robert Mattmüller
Keyword(s):  
Computational Complexity ◽  
Common Knowledge ◽  
Path Finding ◽  
Multi Agent

In multi-agent path finding, it is usually assumed that planning is performed centrally and that the destinations of the agents are common knowledge. We will drop both assumptions and analyze under which conditions it can be guaranteed that the agents reach their respective destinations using implicitly coordinated plans without communication.

scholarly journals Implicitly Coordinated Multi-Agent Path Finding under Destination Uncertainty: Success Guarantees and Computational Complexity

2019 ◽  
Vol 64 ◽  
pp. 497-527 ◽  
Author(s):  
Bernhard Nebel ◽  
Thomas Bolander ◽  
Thorsten Engesser ◽  
Robert Mattmüller
Keyword(s):  
Computational Complexity ◽  
Common Knowledge ◽  
Path Finding ◽  
Computational Costs ◽  
Complete Problems ◽  
Multi Agent ◽  
Np Complete

In multi-agent path finding (MAPF), it is usually assumed that planning is performed centrally and that the destinations of the agents are common knowledge. We will drop both assumptions and analyze under which conditions it can be guaranteed that the agents reach their respective destinations using implicitly coordinated plans without communication. Furthermore, we will analyze what the computational costs associated with such a coordination regime are. As it turns out, guarantees can be given assuming that the agents are of a certain type. However, the implied computational costs are quite severe. In the distributed setting, we either have to solve a sequence of NP-complete problems or have to tolerate exponentially longer executions. In the setting with destination uncertainty, bounded plan existence becomes PSPACE-complete. This clearly demonstrates the value of communicating about plans before execution starts.

scholarly journals Implicit Coordination Using FOND Planning

2020 ◽  
Vol 34 (05) ◽  
pp. 7151-7159
Author(s):  
Thorsten Engesser ◽  
Tim Miller
Keyword(s):  
Computational Complexity ◽  
The Other ◽  
Path Finding ◽  
Initial State ◽  
Arbitrary Initial State ◽  
State Uncertainty ◽  
Multi Agent ◽  
Decidable Fragment ◽  
Implicit Coordination

Epistemic planning can be used to achieve implicit coordination in cooperative multi-agent settings where knowledge and capabilities are distributed between the agents. In these scenarios, agents plan and act on their own without having to agree on a common plan or protocol beforehand. However, epistemic planning is undecidable in general. In this paper, we show how implicit coordination can be achieved in a simpler, propositional setting by using nondeterminism as a means to allow the agents to take the other agents' perspectives. We identify a decidable fragment of epistemic planning that allows for arbitrary initial state uncertainty and non-determinism, but where actions can never increase the uncertainty of the agents. We show that in this fragment, planning for implicit coordination can be reduced to a version of fully observable nondeterministic (FOND) planning and that it thus has the same computational complexity as FOND planning. We provide a small case study, modeling the problem of multi-agent path finding with destination uncertainty in FOND, to show that our approach can be successfully applied in practice.

scholarly journals Multi-Agent Path Finding with Deadlines

2018 ◽  
Author(s):  
Hang Ma ◽  
Glenn Wagner ◽  
Ariel Felner ◽  
Jiaoyang Li ◽  
T. K. Satish Kumar ◽  
...  
Keyword(s):  
Flow Problem ◽  
Search Algorithms ◽  
The Other ◽  
Path Finding ◽  
Combinatorial Search ◽  
Programming Formulation ◽  
Commodity Flow ◽  
Linear Programming Formulation ◽  
Multi Agent ◽  
Integer Linear Programming Formulation

We formalize Multi-Agent Path Finding with Deadlines (MAPF-DL). The objective is to maximize the number of agents that can reach their given goal vertices from their given start vertices within the deadline, without colliding with each other. We first show that MAPF-DL is NP-hard to solve optimally. We then present two classes of optimal algorithms, one based on a reduction of MAPF-DL to a flow problem and a subsequent compact integer linear programming formulation of the resulting reduced abstracted multi-commodity flow network and the other one based on novel combinatorial search algorithms. Our empirical results demonstrate that these MAPF-DL solvers scale well and each one dominates the other ones in different scenarios.

scholarly journals What Can You Do with a Rock? Affordance Extraction via Word Embeddings

2017 ◽  
Author(s):  
Nancy Fulda ◽  
Daniel Ricks ◽  
Ben Murdoch ◽  
David Wingate
Keyword(s):  
Reinforcement Learning ◽  
Computational Complexity ◽  
Linear Algebra ◽  
Autonomous Agents ◽  
Common Knowledge ◽  
Search Space ◽  
Word Embeddings ◽  
Knowledge Database ◽  
Learning Agent ◽  
Action Spaces

Autonomous agents must often detect affordances: the set of behaviors enabled by a situation. Affordance extraction is particularly helpful in domains with large action spaces, allowing the agent to prune its search space by avoiding futile behaviors. This paper presents a method for affordance extraction via word embeddings trained on a tagged Wikipedia corpus. The resulting word vectors are treated as a common knowledge database which can be queried using linear algebra. We apply this method to a reinforcement learning agent in a text-only environment and show that affordance-based action selection improves performance in most cases. Our method increases the computational complexity of each learning step but significantly reduces the total number of steps needed. In addition, the agent's action selections begin to resemble those a human would choose.

A logic of knowledge based on abstract arguments

2021 ◽  
Author(s):  
Yì N Wáng ◽  
Xu Li
Keyword(s):  
Computational Complexity ◽  
Model Checking ◽  
True Belief ◽  
Logical Omniscience ◽  
Knowledge Based ◽  
Logic Of Knowledge ◽  
Multi Agent ◽  
Soundness And Completeness ◽  
Base Logic

Abstract We introduce a logic of knowledge in a framework in which knowledge is treated as a kind of belief. The framework is based on a standard KD45 characterization of belief, and the characterization of knowledge undergoes the classical tripartite analysis that knowledge is justified true belief, which has a natural link to the studies of logics of evidence and justification. The interpretation of knowledge avoids the unwanted properties of logical omniscience, independent of the choice of the base logic of belief. We axiomatize the logic, prove its soundness and completeness and study the computational complexity results of the model checking and satisfiability problems. We extend the logic to a multi-agent setting and introduce a variant in which belief is characterized in a weaker system to avoid the problem of logical omniscience.

Adversarial Multi-Agent Path Finding is Intractable

2021 ◽  
Author(s):  
Marika Ivanova ◽  
Pavel Surynek
Keyword(s):  
Path Finding ◽  
Multi Agent
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