Optimizing decision trees through heuristically guided search

Martelli, Alberto; Montanari, Ugo

doi:10.1145/359657.359664

Cited by 124 publications

(49 citation statements)

References 9 publications

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“…The classical AO * Algorithm for searching AO trees Slagle 1971, Martelli andMontanari 1978) improves upon the brute force approach by utilizating admissible lower bounds h lower N → ≥0 , called heuristic labels, which are lower bounds that are guaranteed not to overestimate the true label of any node. These lower bounds guide the search in a top-down fashion so that only a small portion of the complete AO tree is examined.…”

Section: Ao Treesmentioning

confidence: 99%

An AO^* Based Exact Algorithm for the Canadian Traveler Problem

Aksakallı

Sahin

Arı

2016

INFORMS Journal on Computing

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T he Canadian traveler problem (CTP) is a simple, yet challenging, stochastic optimization problem wherein an agent is given a graph where some edges are blocked with certain probabilities and the status of these edges can be disambiguated dynamically upon reaching an incident vertex. The goal is to devise a traversal policy that results in the shortest expected walk length between a given starting vertex and a termination vertex. CTP has been shown to be intractable in many broad settings. In this paper, we introduce an optimal algorithm for the problem based on a Markov decision process formulation, which is a new improvement on AO * search that takes advantage of the special problem structure in CTP. We call our algorithm CAO * , which stands for AO * with caching. CAO * uses a caching mechanism to avoid re-expansion of previously visited states and makes use of admissible upper bounds at a node level for dynamic state-space pruning. CAO * is not polynomial time, but it can dramatically shorten the execution time needed to find an exact solution for moderately sized instances. We present computational experiments on a realistic variant of the problem involving an actual maritime minefield data set.

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Section: Ao Treesmentioning

confidence: 99%

An AO^* Based Exact Algorithm for the Canadian Traveler Problem

Aksakallı

Sahin

Arı

2016

INFORMS Journal on Computing

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“…There are many efficient algorithms to find optimal solutions in AND/OR graphs and the one used in this work is AO* [Nilsson, 1980], [Martelli and Montanari, 1978]. AO* is a heuristic search algorithm that finds the optimal solution to an implicit AND/OR graph Γ specified by a start state and a successor function.…”

Section: Search Algorithmmentioning

confidence: 99%

Automotive Applications

2009

Optical Science and Engineering

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The aim of this thesis is to contribute to improved diagnosis of automotive vehicles. The work is driven by case studies, where problems and challenges are identified. To solve these problems, theoretically sound and general methods are developed. The methods are then applied to the real world systems.To fulfill performance requirements automotive vehicles are becoming increasingly complex products. This makes them more difficult to diagnose. At the same time, the requirements on the diagnosis itself are steadily increasing. Environmental legislation requires that smaller deviations from specified operation must be detected earlier. More accurate diagnostic methods can be used to reduce maintenance costs and increase uptime. Improved diagnosis can also reduce safety risks related to vehicle operation. Fault diagnosis is the task of identifying possible faults given current observations from the systems. To do this, the internal relations between observations and faults must be identified. In complex systems, such as automotive vehicles, finding these relations is a most challenging problem due to several sources of uncertainty. Observations from the system are often hidden in considerable levels of noise. The systems are complicated to model both since they are complex and since they are operated in continuously changing surroundings. Furthermore, since faults typically are rare, and sometimes never described, it is often difficult to get hold of enough data to learn the relations from.Due to the several sources of uncertainty in fault diagnosis of automotive systems, a probabilistic approach is used, both to find the internal relations, and to identify the faults possibly present in the system given the current observations. To do this successfully, all available information is integrated in the computations.Both on-board and off-board diagnosis are considered. The two tasks may seem different in nature: on-board diagnosis is performed without human integration, while the off-board diagnosis is mainly based on the interactivity with a mechanic. On the other hand, both tasks regard the same vehicle, and information from the on-board diagnosis system may be useful also for off-board diagnosis. The probabilistic methods are general, and it is natural to consider both tasks.The thesis contributes in three main areas. First, in Paper 1 and 2, methods are developed for combining training data and expert knowledge of different kinds to compute probabilities for faults. These methods are primarily developed with on-board diagnosis in mind, but are also applicable to off-board diagnosis. The methods are general, and can be used not only in diagnosis of technical system, but also in many other applications, including medical diagnosis and econometrics, where both data and expert knowledge are present.The second area concerns inference in off-board diagnosis and troubleshooting, and the contribution consists in the methods developed in Paper 3 and 4.ii The methods handle probability computations in systems subject...

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“…Besides, there have been many research on AND/OR graph searching such as AO* algorithm (A. Martelli, 1978) [4] [5]. Gu [2] presented a fast service composition model using a inverted table to index the services.…”

Section: Related Workmentioning

confidence: 99%