## Adaptive Parallel Iterative Deepening Search (1998)

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Venue: | Journal of Artificial Intelligence Research |

Citations: | 9 - 0 self |

### BibTeX

@ARTICLE{Cook98adaptiveparallel,

author = {Diane J. Cook and R. Craig Varnell},

title = {Adaptive Parallel Iterative Deepening Search},

journal = {Journal of Artificial Intelligence Research},

year = {1998},

volume = {9},

pages = {139--166}

}

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### Abstract

Many of the artificial intelligence techniques developed to date rely on heuristic search through large spaces. Unfortunately, the size of these spaces and the corresponding computational effort reduce the applicability of otherwise novel and effective algorithms. A number of parallel and distributed approaches to search have considerably improved the performance of the search process. Our goal is to develop an architecture that automatically selects parallel search strategies for optimal performance on a variety of search problems. In this paper we describe one such architecture realized in the Eureka system, which combines the benefits of many different approaches to parallel heuristic search. Through empirical and theoretical analyses we observe that features of the problem space directly affect the choice of optimal parallel search strategy. We then employ machine learning techniques to select the optimal parallel search strategy for a given problem space. When a new search task is...

### Citations

4934 |
C4.5: Programs for Machine Learning
- Quinlan
- 1993
(Show Context)
Citation Context ...tion of the problem instance for a given strategy choice. 3. Problem attributes are combined with the corresponding classes and are fed as training examples to a machine learning system. We use C4.5 (=-=Quinlan, 1993-=-) to induce a decision tree from the pre-classified cases. A rule base is generated for each concept to be learned, corresponding to each of the strategy decisions listed above that need to be made. 4... |

3354 | Induction of Decision Trees
- Quinlan
- 1986
(Show Context)
Citation Context ...isting approaches, we supplied the data from all of the 15 Puzzle classification experiments described in the previous section as input to versions of C4.5, the ID3 decision tree induction algorithm (=-=Quinlan, 1986-=-), the CN2 sequential covering algorithm (Clark & Niblett, 1989), a backpropagation neural net (Rumelhart & McClelland, 1986), a Bayesian classifier (Cestnik, 1990), and a majority-wins classifier. As... |

747 | The CN2 induction algorithm - Clark, Niblett - 1989 |

471 | Parallel distributed processing: explorations in the microstructure of cognition - Rumelhart, McClelland - 1986 |

172 | Introduction to Robotics - Craig - 2005 |

166 |
Estimating probabilities: A crucial task in machine learning
- Cestnik
- 1990
(Show Context)
Citation Context ...3 decision tree induction algorithm (Quinlan, 1986), the CN2 sequential covering algorithm (Clark & Niblett, 1989), a backpropagation neural net (Rumelhart & McClelland, 1986), a Bayesian classifier (=-=Cestnik, 1990-=-), and a majority-wins classifier. As with the other experiments, results are based on ten-fold cross-validation. Table 14 shows that the decision tree algorithms performed best on this particular dat... |

126 | An Architecture for Active Networking - Calvert, Zegura - 1997 |

91 | Automatically configuring constraint satisfaction programs: A case study - Minton - 1996 |

84 | Partial order planning: Evaluating possible efficiency gains - Barrett, Weld - 1994 |

62 | Integrating user interface agents with conventional applications
- Lieberman
- 1998
(Show Context)
Citation Context ...le environments applied to computer networks (Bhattacharjee, Calvert, & Zegura, 1997; Steenkiste, Fisher, & Zhang, 1997) and to interactive humancomputer interfaces (Frank, Sukavirija, & Foley, 1995; =-=Lieberman, 1998-=-). This work is unique in allowing both problem-specific and architecture-specific features to influence the choice of strategies and in applying adaptive software techniques to parallel search. Eurek... |

37 |
Scalable Parallel Formulations of Depth-First Search
- Kumar, Rao
- 1990
(Show Context)
Citation Context ...e increases in performance to such compute-intensive tasks. In response, a number of parallel approaches have been developed to improve various search algorithms including depth-first search (Kumar & =-=Rao, 1990-=-), branch-and-bound search (Agrawal, Janakiram, & Mehrotra, 1988), A* (Evett, Hendler, Mahanti, & Nau, 1995; Mahapatra & Dutt, 1995), IDA* (Mahanti & Daniels, 1993; Powley, Ferguson, & Korf, 1993; Pow... |

35 | Parallel search algorithms for robot motion planning - Challou, Gini, et al. - 1993 |

35 | Unstructured tree search on simd parallel computers - Karypis, Kumar - 1994 |

31 | Studying overheads in massively parallel min/max-tree evaluations - Feldmann, Mysliwietz, et al. - 1994 |

27 | A Parallel Implementation of Iterative-Deepening A - Rao, Kumar, et al. - 1987 |

26 | Depth-First Heuristic Search on a SIMD Machine - Powley, Ferguson, et al. - 1993 |

23 |
Single-agent parallel window search
- Powley, Korf
- 1991
(Show Context)
Citation Context ...nch-and-bound search (Agrawal, Janakiram, & Mehrotra, 1988), A* (Evett, Hendler, Mahanti, & Nau, 1995; Mahapatra & Dutt, 1995), IDA* (Mahanti & Daniels, 1993; Powley, Ferguson, & Korf, 1993; Powley & =-=Korf, 1991-=-), and game tree search (Feldmann, Mysliwietz, & Monien, 1994), as well as to improve the run time of specific applications such as the fifteen puzzle problem (Kumar & Rao, 1990) and robot arm path pl... |

18 | A multi-level load balancing scheme for or-parallel exhaustive search programs on the multi-psi - Furuichi, Taki, et al. - 1990 |

16 | PRA*: Massively Parallel Heuristic Search
- Evett, Hendler, et al.
- 1995
(Show Context)
Citation Context ...ell distributed-memory algorithms, parallel search algorithms have been developed for MIMD shared-memory systems (Kale & Saletore, 1990; Kumar & Rao, 1990) and SIMD architectures (Cook & Lyons, 1993; =-=Evett et al., 1995-=-; Karypis & Kumar, 1992; Mahanti & Daniels, 1993; Powley et al., 1993). While existing approaches to parallel search have many contributions to offer, comparing these approaches and determining the be... |

14 | V.: AIDA* – Asynchronous Parallel IDA - Reinefeld, Schnecke - 1994 |

12 |
A.: A distributive and adaptive dynamic load balancing scheme for parallel processing of mediumgrain tasks
- Saletore
- 1990
(Show Context)
Citation Context ...s Foundation and Morgan Kaufmann Publishers. All rights reserved. Cook and Varnell distributed-memory algorithms, parallel search algorithms have been developed for MIMD shared-memory systems (Kale & =-=Saletore, 1990-=-; Kumar & Rao, 1990) and SIMD architectures (Cook & Lyons, 1993; Evett et al., 1995; Karypis & Kumar, 1992; Mahanti & Daniels, 1993; Powley et al., 1993). While existing approaches to parallel search ... |

10 | Inference bear: designing interactive interfaces through before and after snapshots - Frank, Sukavirija, et al. - 1995 |

9 | Maximizing the benefits of parallel search using machine learning
- Cook, Varnell
- 1997
(Show Context)
Citation Context ...can be more effective when the branching factor is very large and the number of IDA* iterations is relatively small. A compromise between these approaches divides the set of processors into clusters (=-=Cook, 1997-=-). Each cluster is given a unique cost threshold, and the search space is divided between processors within each cluster, as shown in Figure 3. Setting the number of clusters to one simulates distribu... |

8 | Parallel State-Space Search for a First Solution with Consistent Linear Speedups - Kale, Saletore - 1990 |

8 | SIMD parallel heuristic search - Mahanti, Daniels - 1992 |

8 | New anticipatory load balancing strategies for parallel A* algorithms - Mahapatra, Dutt - 1995 |

7 | A randomized Parallel Branch and Bound Algorithm - Janakiram, Agrawal, et al. - 1988 |

7 |
Parallel branch-and-bound algorithms on the hypercube
- Anderson, Chen
- 1987
(Show Context)
Citation Context ...iate a load balance operation (Furuichi, Taki, & Ichyoshi, 1990; Rajpal & Kumar, 1993) or allow all processors to periodically shift work to keep the average load within acceptable bounds (Anderson & =-=Chen, 1987-=-; Saletore, 1990). 2.3 Tree Ordering Problem solutions can exist anywhere in the search space. Using IDA* search, the children are expanded in a depth-first manner from left to right, bounded in depth... |

6 | A hybrid approach to improving the performance of parallel search
- Cook
- 1996
(Show Context)
Citation Context ...can be more effective when the branching factor is very large and the number of IDA* iterations is relatively small. A compromise between these approaches divides the set of processors into clusters (=-=Cook, 1997-=-). Each cluster is given a unique cost threshold, and the search space is divided between processors within each cluster, as shown in Figure 3. Setting the number of clusters to one simulates distribu... |

6 | Resource Management for Application-Aware Networks - Steenkiste, Fisher, et al. |

5 | Parallel search using transformation-ordering iterative-deepening A - Cook, Hall, et al. - 1993 |

4 | Static partitioning with slackness - Suttner - 1995 |

2 | Massively parallel IDA* search - Cook, Lyons - 1993 |

2 | Parallel heuristic search algorithms for message passing multiprocessor systems - Rajpal - 1993 |

1 | Adaptive Parallel Iterative Deepening Search - Frank, Sukavirija - 1995 |

1 |
Adaptive Parallel Iterative Deepening Search
- Korf
- 1991
(Show Context)
Citation Context ...nch-and-bound search (Agrawal, Janakiram, & Mehrotra, 1988), A* (Evett, Hendler, Mahanti, & Nau, 1995; Mahapatra & Dutt, 1995), IDA* (Mahanti & Daniels, 1993; Powley, Ferguson, & Korf, 1993; Powley & =-=Korf, 1991-=-), and game tree search (Feldmann, Mysliwietz, & Monien, 1994), as well as to improve the run time of specific applications such as the fifteen puzzle problem (Kumar & Rao, 1990) and robot arm path pl... |