Toward State Space Island Identification in Multi-process Bidirectional Heuristic Search

2009 ◽  
Author(s):  
Anestis A. Toptsis ◽  
Rahul A. Chaturvedi
Keyword(s):  
State Space ◽  
Heuristic Search

Heuristics for Domain-Independent Planning: A Survey

2011 ◽  
Vol 135-136 ◽  
pp. 573-577 ◽  
Author(s):  
Rui Shi Liang ◽  
Min Huang
Keyword(s):  
State Space ◽  
Heuristic Search ◽  
Research Work ◽  
Future Research ◽  
Survey Analysis ◽  
Work Related ◽  
State Space Search ◽  
Survey Results ◽  
Domain Independent ◽  
Intense Research

Increasing interest has been devoted to Planning as Heuristic Search over the years. Intense research has focused on deriving fast and accurate heuristics for domain-independent planning. This paper reports on an extensive survey and analysis of research work related to heuristic derivation techniques for state space search. Survey results reveal that heuristic techniques have been extensively applied in many efficient planners and result in impressive performances. We extend the survey analysis to suggest promising avenues for future research in heuristic derivation and heuristic search techniques.

scholarly journals Heuristic search strategies for multiobjective state space search

Sadhana ◽  
1996 ◽  
Vol 21 (3) ◽  
pp. 263-290
Author(s):  
Pallab Dasgupta ◽  
P P Chakrabarti ◽  
S C Desarkar
Keyword(s):  
State Space ◽  
Heuristic Search ◽  
Search Strategies ◽  
State Space Search

scholarly journals Scrubbing During Learning In Real-time Heuristic Search

2016 ◽  
Vol 57 ◽  
pp. 307-343 ◽  
Author(s):  
Nathan R. Sturtevant ◽  
Vadim Bulitko
Keyword(s):  
State Space ◽  
Real Time ◽  
Lower Bounds ◽  
Heuristic Search ◽  
Search Algorithm ◽  
The State ◽  
Worst Case ◽  
Heuristic Search Algorithm ◽  
Practical Performance ◽  
Theoretical Results

Real-time agent-centered heuristic search is a well-studied problem where an agent that can only reason locally about the world must travel to a goal location using bounded computation and memory at each step. Many algorithms have been proposed for this problem and theoretical results have also been derived for the worst-case performance with simple examples demonstrating worst-case performance in practice. Lower bounds, however, have not been widely studied. In this paper we study best-case performance more generally and derive theoretical lower bounds for reaching the goal using LRTA*, a canonical example of a real-time agent-centered heuristic search algorithm. The results show that, given some reasonable restrictions on the state space and the heuristic function, the number of steps an LRTA*-like algorithm requires to reach the goal will grow asymptotically faster than the state space, resulting in ``scrubbing'' where the agent repeatedly visits the same state. We then show that while the asymptotic analysis does not hold for more complex real-time search algorithms, experimental results suggest that it is still descriptive of practical performance.

scholarly journals Best-First Heuristic Search for Multicore Machines

2010 ◽  
Vol 39 ◽  
pp. 689-743 ◽  
Author(s):  
E. Burns ◽  
S. Lemons ◽  
W. Ruml ◽  
R. Zhou
Keyword(s):  
State Space ◽  
Shared Memory ◽  
Temporal Logic ◽  
Heuristic Search ◽  
Multicore Processors ◽  
The State ◽  
New Method ◽  
Parallel Search ◽  
Empirical Comparison ◽  
Best First Search

To harness modern multicore processors, it is imperative to develop parallel versions of fundamental algorithms. In this paper, we compare different approaches to parallel best-first search in a shared-memory setting. We present a new method, PBNF, that uses abstraction to partition the state space and to detect duplicate states without requiring frequent locking. PBNF allows speculative expansions when necessary to keep threads busy. We identify and fix potential livelock conditions in our approach, proving its correctness using temporal logic. Our approach is general, allowing it to extend easily to suboptimal and anytime heuristic search. In an empirical comparison on STRIPS planning, grid pathfinding, and sliding tile puzzle problems using 8-core machines, we show that A*, weighted A* and Anytime weighted A* implemented using PBNF yield faster search than improved versions of previous parallel search proposals.

scholarly journals The FF Planning System: Fast Plan Generation Through Heuristic Search

2001 ◽  
Vol 14 ◽  
pp. 253-302 ◽  
Author(s):  
J. Hoffmann ◽  
B. Nebel
Keyword(s):  
State Space ◽  
Heuristic Search ◽  
Search Strategy ◽  
Search Space ◽  
Systematic Search ◽  
Hill Climbing ◽  
Planning System ◽  
Plan Generation ◽  
State Space Search ◽  
Algorithmic Techniques

We describe and evaluate the algorithmic techniques that are used in the FF planning system. Like the HSP system, FF relies on forward state space search, using a heuristic that estimates goal distances by ignoring delete lists. Unlike HSP's heuristic, our method does not assume facts to be independent. We introduce a novel search strategy that combines hill-climbing with systematic search, and we show how other powerful heuristic information can be extracted and used to prune the search space. FF was the most successful automatic planner at the recent AIPS-2000 planning competition. We review the results of the competition, give data for other benchmark domains, and investigate the reasons for the runtime performance of FF compared to HSP.

scholarly journals Distributional Metareasoning for Heuristic Search

2021 ◽  
Author(s):  
Tianyi Gu
Keyword(s):  
State Space ◽  
Real Time ◽  
Real World ◽  
Heuristic Search ◽  
Time Pressure ◽  
Autonomous Systems ◽  
Optimal Solutions ◽  
Algorithmic Approach ◽  
Heuristic Search Methods ◽  
Computational Resources

Heuristic search methods are widely used in many real-world autonomous systems. Yet, people always want to solve search problems that are larger than time allows. To address these challenging problems, even suboptimally, a planning agent should be smart enough to intelligently allocate its computational resources, to think carefully about where in the state space it should spend time searching. For finding optimal solutions, we must examine every node that is not provably too expensive. In contrast, to find suboptimal solutions when under time pressure, we need to be very selective about which nodes to examine. In this work, we will demonstrate that estimates of uncertainty, represented as belief distributions, can be used to drive search effectively. This type of algorithmic approach is known as metareasoning, which refers to reasoning about which reasoning to do. We will provide examples of improved algorithms for real-time search, bounded-cost search, and situated planning.

scholarly journals Subgoaling Techniques for Satisficing and Optimal Numeric Planning

2020 ◽  
Vol 68 ◽  
pp. 691-752
Author(s):  
Enrico Scala ◽  
Patrik Haslum ◽  
Sylvie Thiébaux ◽  
Miquel Ramirez
Keyword(s):  
State Space ◽  
Heuristic Search ◽  
State Of The Art ◽  
Planning Problem ◽  
State Variables ◽  
The Core ◽  
State Space Search ◽  
Theoretical Assumptions ◽  
Good Trade ◽  
Core Idea

This paper studies novel subgoaling relaxations for automated planning with propositional and numeric state variables. Subgoaling relaxations address one source of complexity of the planning problem: the requirement to satisfy conditions simultaneously. The core idea is to relax this requirement by recursively decomposing conditions into atomic subgoals that are considered in isolation. Such relaxations are typically used for pruning, or as the basis for computing admissible or inadmissible heuristic estimates to guide optimal or satis_cing heuristic search planners. In the last decade or so, the subgoaling principle has underpinned the design of an abundance of relaxation-based heuristics whose formulations have greatly extended the reach of classical planning. This paper extends subgoaling relaxations to support numeric state variables and numeric conditions. We provide both theoretical and practical results, with the aim of reaching a good trade-o_ between accuracy and computation costs within a heuristic state-space search planner. Our experimental results validate the theoretical assumptions, and indicate that subgoaling substantially improves on the state of the art in optimal and satisficing numeric planning via forward state-space search.

NAVIGATION AMONG MOVABLE OBSTACLES: REAL-TIME REASONING IN COMPLEX ENVIRONMENTS

2005 ◽  
Vol 02 (04) ◽  
pp. 479-503 ◽  
Author(s):  
MIKE STILMAN ◽  
JAMES J. KUFFNER
Keyword(s):  
Mobile Robots ◽  
State Space ◽  
Real Time ◽  
Heuristic Search ◽  
Free Space ◽  
Single Agent ◽  
Complex Environments ◽  
Moving Obstacles ◽  
Large Numbers ◽  
Object Domain

In this paper, we address the problem of Navigation Among Movable Obstacles (NAMO): a practical extension to navigation for humanoids and other dexterous mobile robots. The robot is permitted to reconfigure the environment by moving obstacles and clearing free space for a path. This paper presents a resolution complete planner for a subclass of NAMO problems. Our planner takes advantage of the navigational structure through state-space decomposition and heuristic search. The planning complexity is reduced to the difficulty of the specific navigation task, rather than the dimensionality of the multi-object domain. We demonstrate real-time results for spaces that contain large numbers of movable obstacles. We also present a practical framework for single-agent search that can be used in algorithmic reasoning about this domain.

scholarly journals A Heuristic Search Approach to Planning with Continuous Resources in Stochastic Domains

2009 ◽  
Vol 34 ◽  
pp. 27-59 ◽  
Author(s):  
N. Meuleau ◽  
E. Benazera ◽  
R. I. Brafman ◽  
E. A. Hansen ◽  
Mausam
Keyword(s):  
State Space ◽  
Heuristic Search ◽  
Resource Constraints ◽  
Computational Effort ◽  
The State ◽  
Continuous Variables ◽  
State Variables ◽  
Heuristic Search Algorithms ◽  
Continuous State ◽  
Stochastic Domains

We consider the problem of optimal planning in stochastic domains with resource constraints, where the resources are continuous and the choice of action at each step depends on resource availability. We introduce the HAO* algorithm, a generalization of the AO* algorithm that performs search in a hybrid state space that is modeled using both discrete and continuous state variables, where the continuous variables represent monotonic resources. Like other heuristic search algorithms, HAO* leverages knowledge of the start state and an admissible heuristic to focus computational effort on those parts of the state space that could be reached from the start state by following an optimal policy. We show that this approach is especially effective when resource constraints limit how much of the state space is reachable. Experimental results demonstrate its effectiveness in the domain that motivates our research: automated planning for planetary exploration rovers.

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