scholarly journals Searching with Consistent Prioritization for Multi-Agent Path Finding

2019 ◽  
Vol 33 ◽  
pp. 7643-7650 ◽  
Author(s):  
Hang Ma ◽  
Daniel Harabor ◽  
Peter J. Stuckey ◽  
Jiaoyang Li ◽  
Sven Koenig
Keyword(s):  
State Of The Art ◽  
A Priori ◽  
Path Finding ◽  
Combinatorial Search ◽  
Random Assignment ◽  
Success Rates ◽  
Multi Agent ◽  
Priority Ordering ◽  
First Time ◽  
Theoretical Results

We study prioritized planning for Multi-Agent Path Finding (MAPF). Existing prioritized MAPF algorithms depend on rule-of-thumb heuristics and random assignment to determine a fixed total priority ordering of all agents a priori. We instead explore the space of all possible partial priority orderings as part of a novel systematic and conflict-driven combinatorial search framework. In a variety of empirical comparisons, we demonstrate state-of-the-art solution qualities and success rates, often with similar runtimes to existing algorithms. We also develop new theoretical results that explore the limitations of prioritized planning, in terms of completeness and optimality, for the first time.

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 Explanation Generation for Multi-Modal Multi-Agent Path Finding with Optimal Resource Utilization using Answer Set Programming

2020 ◽  
Vol 20 (6) ◽  
pp. 974-989
Author(s):  
AYSU BOGATARKAN ◽  
ESRA ERDEM
Keyword(s):  
Answer Set Programming ◽  
Search Problem ◽  
Path Finding ◽  
Combinatorial Search ◽  
Transportation Modes ◽  
Optimal Resource ◽  
Multi Agent ◽  
Nonexistence Of Solutions ◽  
Velocity Constraints ◽  
Answer Set

AbstractThe multi-agent path finding (MAPF) problem is a combinatorial search problem that aims at finding paths for multiple agents (e.g., robots) in an environment (e.g., an autonomous warehouse) such that no two agents collide with each other, and subject to some constraints on the lengths of paths. We consider a general version of MAPF, called mMAPF, that involves multi-modal transportation modes (e.g., due to velocity constraints) and consumption of different types of resources (e.g., batteries). The real-world applications of mMAPF require flexibility (e.g., solving variations of mMAPF) as well as explainability. Our earlier studies on mMAPF have focused on the former challenge of flexibility. In this study, we focus on the latter challenge of explainability, and introduce a method for generating explanations for queries regarding the feasibility and optimality of solutions, the nonexistence of solutions, and the observations about solutions. Our method is based on answer set programming.

Sensorless In-Hand Manipulation by An Underactuated Robot Hand

2020 ◽  
Vol 12 (5) ◽  
Author(s):  
Diego Ospina ◽  
Alejandro Ramirez-Serrano
Keyword(s):  
State Of The Art ◽  
A Priori ◽  
Experimental Tests ◽  
Robot Hand ◽  
A Priori Information ◽  
Robot Hands ◽  
Priori Information ◽  
Underactuated Robot ◽  
Theoretical Results ◽  
Manipulation Strategy

Abstract The limited dexterity that existing hand prostheses provide to users contrasts with the manipulation abilities exhibited by state-of-the-art robot hands. This paper presents an underactuated robot hand with in-hand manipulation capabilities to demonstrate the use of underactuation in the development of effective hand replacements. This paper describes a specific underactuated hand architecture, representative of many existing underactuated hand prototypes. First, the hand is modeled and its ability to manipulate objects of different geometries is analyzed. Second, a manipulation strategy suitable for prosthetic applications is proposed. The strategy enables the hand to manipulate objects in-hand without any a priori information of their geometry or physical properties. Finally, experimental tests conducted to validate the theoretical results are presented.

scholarly journals Multi-Agent Path Finding for Large Agents

2019 ◽  
Vol 33 ◽  
pp. 7627-7634 ◽  
Author(s):  
Jiaoyang Li ◽  
Pavel Surynek ◽  
Ariel Felner ◽  
Hang Ma ◽  
T. K. Satish Kumar ◽  
...  
Keyword(s):  
Real World ◽  
State Of The Art ◽  
Tree Search ◽  
Multiple Constraints ◽  
Path Finding ◽  
Multiple Points ◽  
Admissible Heuristics ◽  
Single Location ◽  
Multi Agent ◽  
High Level

Multi-Agent Path Finding (MAPF) has been widely studied in the AI community. For example, Conflict-Based Search (CBS) is a state-of-the-art MAPF algorithm based on a twolevel tree-search. However, previous MAPF algorithms assume that an agent occupies only a single location at any given time, e.g., a single cell in a grid. This limits their applicability in many real-world domains that have geometric agents in lieu of point agents. Geometric agents are referred to as “large” agents because they can occupy multiple points at the same time. In this paper, we formalize and study LAMAPF, i.e., MAPF for large agents. We first show how CBS can be adapted to solve LA-MAPF. We then present a generalized version of CBS, called Multi-Constraint CBS (MCCBS), that adds multiple constraints (instead of one constraint) for an agent when it generates a high-level search node. We introduce three different approaches to choose such constraints as well as an approach to compute admissible heuristics for the high-level search. Experimental results show that all MC-CBS variants outperform CBS by up to three orders of magnitude in terms of runtime. The best variant also outperforms EPEA* (a state-of-the-art A*-based MAPF solver) in all cases and MDD-SAT (a state-of-the-art reduction-based MAPF solver) in some cases.

scholarly journals Unifying Search-based and Compilation-based Approaches to Multi-agent Path Finding through Satisfiability Modulo Theories

2019 ◽  
Author(s):  
Pavel Surynek
Keyword(s):  
Undirected Graph ◽  
State Of The Art ◽  
The State ◽  
Satisfiability Modulo Theories ◽  
Path Finding ◽  
Low Level ◽  
Multi Agent ◽  
High Level ◽  
Novel Algorithm

We unify search-based and compilation-based approaches to multi-agent path finding (MAPF) through satisfiability modulo theories (SMT). The task in MAPF is to navigate agents in an undirected graph to given goal vertices so that they do not collide. We rephrase Conflict-Based Search (CBS), one of the state-of-the-art algorithms for optimal MAPF solving, in the terms of SMT. This idea combines SAT-based solving known from MDD-SAT, a SAT-based optimal MAPF solver, at the low-level with conflict elimination of CBS at the high-level. Where the standard CBS branches the search after a conflict, we refine the propositional model with a disjunctive constraint. Our novel algorithm called SMT-CBS hence does not branch at the high-level but incrementally extends the propositional model. We experimentally compare SMT-CBS with CBS, ICBS, and MDD-SAT.

scholarly journals Symmetry-Breaking Constraints for Grid-Based Multi-Agent Path Finding

2019 ◽  
Vol 33 ◽  
pp. 6087-6095 ◽  
Author(s):  
Jiaoyang Li ◽  
Daniel Harabor ◽  
Peter J. Stuckey ◽  
Hang Ma ◽  
Sven Koenig
Keyword(s):  
Symmetry Breaking ◽  
State Of The Art ◽  
Search Space ◽  
Single Step ◽  
Path Finding ◽  
Current Path ◽  
Rate Versus ◽  
Multi Agent ◽  
Specialized Technique ◽  
A Current

We describe a new way of reasoning about symmetric collisions for Multi-Agent Path Finding (MAPF) on 4-neighbor grids. We also introduce a symmetry-breaking constraint to resolve these conflicts. This specialized technique allows us to identify and eliminate, in a single step, all permutations of two currently assigned but incompatible paths. Each such permutation has exactly the same cost as a current path, and each one results in a new collision between the same two agents. We show that the addition of symmetry-breaking techniques can lead to an exponential reduction in the size of the search space of CBS, a popular framework for MAPF, and report significant improvements in both runtime and success rate versus CBSH and EPEA* – two recent and state-of-the-art MAPF algorithms.

scholarly journals A Declarative Method for Dynamic Multi-Agent Path Finding

10.29007/cnzw ◽  
2019 ◽  
Author(s):  
Aysu Bogatarkan ◽  
Volkan Patoglu ◽  
Esra Erdem
Keyword(s):  
Answer Set Programming ◽  
New Method ◽  
Search Problem ◽  
Path Finding ◽  
Combinatorial Search ◽  
Multiple Agents ◽  
New Agents ◽  
Two Agents ◽  
Multi Agent ◽  
Answer Set

The multi-agent path finding (MAPF) problem is a combinatorial search problem that aims at finding paths for multiple agents such that no two agents collide with each other. We study a dynamic variant of MAPF, called D-MAPF, which allows changes in the environment (e.g., some existing obstacles may be removed from the environment or moved to some other location, or new obstacles may be included in the environment), and/or changes in the team (e.g., some existing agents may leave and some new agents may join the team) at different times. We introduce a new method to solve D-MAPF, using answer set programming.

Building Attention and Edge Convolution Neural Networks for Bioactivity and Physical-Chemical Property Prediction

2019 ◽  
Author(s):  
Michael Withnall ◽  
Edvard Lindelöf ◽  
Ola Engkvist ◽  
Hongming Chen
Keyword(s):  
Message Passing ◽  
State Of The Art ◽  
A Priori ◽  
Model Performance ◽  
Learning Approaches ◽  
Physical Chemical ◽  
Chemical Descriptor ◽  
Derived Properties ◽  
Memory Schemes ◽  
Hyperparameter Selection

We introduce Attention and Edge Memory schemes to the existing Message Passing Neural Network framework for graph convolution, and benchmark our approaches against eight different physical-chemical and bioactivity datasets from the literature. We remove the need to introduce <i>a priori</i> knowledge of the task and chemical descriptor calculation by using only fundamental graph-derived properties. Our results consistently perform on-par with other state-of-the-art machine learning approaches, and set a new standard on sparse multi-task virtual screening targets. We also investigate model performance as a function of dataset preprocessing, and make some suggestions regarding hyperparameter selection.

Browser Security Attacks and Detection Techniques: A Case of Tabnabbing

2020 ◽  
Vol 8 (1) ◽  
pp. 33-41
Author(s):  
Dr. S. Sarika ◽  
Keyword(s):  
Credit Card ◽  
State Of The Art ◽  
Experimental Results ◽  
Detection Technique ◽  
Security Attacks ◽  
Agent Based ◽  
Detection Techniques ◽  
Browser Security ◽  
Cyber Threats ◽  
Multi Agent

Phishing is a malicious and deliberate act of sending counterfeit messages or mimicking a webpage. The goal is either to steal sensitive credentials like login information and credit card details or to install malware on a victim’s machine. Browser-based cyber threats have become one of the biggest concerns in networked architectures. The most prolific form of browser attack is tabnabbing which happens in inactive browser tabs. In a tabnabbing attack, a fake page disguises itself as a genuine page to steal data. This paper presents a multi agent based tabnabbing detection technique. The method detects heuristic changes in a webpage when a tabnabbing attack happens and give a warning to the user. Experimental results show that the method performs better when compared with state of the art tabnabbing detection techniques.

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