scholarly journals Multi-Directional Heuristic Search

2020 ◽  
Author(s):  
Dor Atzmon ◽  
Jiaoyang Li ◽  
Ariel Felner ◽  
Eliran Nachmani ◽  
Shahaf Shperberg ◽  
...  
Keyword(s):  
Heuristic Search ◽  
Search Algorithm ◽  
Cost Functions ◽  
Multiple Agents ◽  
Admissible Heuristics ◽  
Heuristic Search Algorithm ◽  
Bidirectional Search ◽  
Multi Agent

In the Multi-Agent Meeting problem (MAM), the task is to find a meeting location for multiple agents, as well as a path for each agent to that location. In this paper, we introduce MM*, a Multi-Directional Heuristic Search algorithm that finds the optimal meeting location under different cost functions. MM* generalizes the Meet in the Middle (MM) bidirectional search algorithm to the case of finding an optimal meeting location for multiple agents. Several admissible heuristics are proposed, and experiments demonstrate the benefits of MM*.

A HYBRID ALGORITHM FOR FORMING THE SHORTEST TRAJECTORY BASED ON THE APPLICATION OF MULTI-AGENT LEARNING WITH REINFORCEMENT, THE SEARCH ALGORITHM A* AND EXCHANGE OF EXPERIENCE

2021 ◽  
pp. 13-26
Author(s):  
Yu. V. Dubenko ◽  
E. E. Dyshkant ◽  
N. N. Timchenko ◽  
N. A. Rudeshko
Keyword(s):  
Reinforcement Learning ◽  
Heuristic Search ◽  
Intelligent Agents ◽  
Hybrid Algorithm ◽  
Search Algorithm ◽  
Heuristic Search Algorithm ◽  
Agent Learning ◽  
Multi Agent ◽  
Multi Level ◽  
Automatic Formation

The article presents a hybrid algorithm for the formation of the shortest trajectory for intelligent agents of a multi-agent system, based on the synthesis of methods of the reinforcement learning paradigm, the heuristic search algorithm A*, which has the functions of exchange of experience, as well as the automatic formation of subgroups of agents based on their visibility areas. The experimental evaluation of the developed algorithm was carried out by simulating the task of finding the target state in the maze in the Microsoft Unity environment. The results of the experiment showed that the use of the developed hybrid algorithm made it possible to reduce the time for solving the problem by an average of 12.7 % in comparison with analogs. The differences between the proposed new “hybrid algorithm for the formation of the shortest trajectory based on the use of multi-agent reinforcement learning, search algorithm A* and exchange of experience” from analogs are as follows: – application of the algorithm for the formation of subgroups of subordinate agents based on the “scope” of the leader agent for the implementation of a multi-level hierarchical system for managing a group of agents; – combining the principles of reinforcement learning and the search algorithm A*.

scholarly journals Front-to-End Bidirectional Heuristic Search with Near-Optimal Node Expansions

2017 ◽  
Author(s):  
Jingwei Chen ◽  
Robert C. Holte ◽  
Sandra Zilles ◽  
Nathan R. Sturtevant
Keyword(s):  
Heuristic Search ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Search Algorithms ◽  
Worst Case ◽  
Heuristic Search Algorithm ◽  
Bidirectional Search ◽  
Optimal Node ◽  
Minimum Number ◽  
Better Than

It is well-known that any admissible unidirectional heuristic search algorithm must expand all states whose f-value is smaller than the optimal solution cost when using a consistent heuristic. Such states are called “surely expanded” (s.e.). A recent study characterized s.e. pairs of states for bidirectional search with consistent heuristics: if a pair of states is s.e. then at least one of the two states must be expanded. This paper derives a lower bound, VC, on the minimum number of expansions required to cover all s.e. pairs, and present a new admissible front-to-end bidirectional heuristic search algorithm, Near-Optimal Bidirectional Search (NBS), that is guaranteed to do no more than 2VC expansions. We further prove that no admissible front-to-end algorithm has a worst case better than 2VC. Experimental results show that NBS competes with or outperforms existing bidirectional search algorithms, and often outperforms A* as well.

scholarly journals A Unifying View on Individual Bounds and Heuristic Inaccuracies in Bidirectional Search

2020 ◽  
Vol 34 (03) ◽  
pp. 2327-2334
Author(s):  
Vidal Alcázar ◽  
Pat Riddle ◽  
Mike Barley
Keyword(s):  
Lower Bound ◽  
Heuristic Search ◽  
Search Algorithm ◽  
Substantial Improvement ◽  
Full Potential ◽  
The Past ◽  
Heuristic Search Algorithms ◽  
Bidirectional Search ◽  
Order Of Magnitude ◽  
The Cost

In the past few years, new very successful bidirectional heuristic search algorithms have been proposed. Their key novelty is a lower bound on the cost of a solution that includes information from the g values in both directions. Kaindl and Kainz (1997) proposed measuring how inaccurate a heuristic is while expanding nodes in the opposite direction, and using this information to raise the f value of the evaluated nodes. However, this comes with a set of disadvantages and remains yet to be exploited to its full potential. Additionally, Sadhukhan (2013) presented BAE∗, a bidirectional best-first search algorithm based on the accumulated heuristic inaccuracy along a path. However, no complete comparison in regards to other bidirectional algorithms has yet been done, neither theoretical nor empirical. In this paper we define individual bounds within the lower-bound framework and show how both Kaindl and Kainz's and Sadhukhan's methods can be generalized thus creating new bounds. This overcomes previous shortcomings and allows newer algorithms to benefit from these techniques as well. Experimental results show a substantial improvement, up to an order of magnitude in the number of necessarily-expanded nodes compared to state-of-the-art near-optimal algorithms in common benchmarks.

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