Showing papers on "Admissible heuristic published in 2004"

Additive pattern database heuristics

Abstract: We explore a method for computing admissible heuristic evaluation functions for search problems. It utilizes pattern databases (Culberson & Schaeffer, 1998), which are precomputed tables of the exact cost of solving various subproblems of an existing problem. Unlike standard pattern database heuristics, however, we partition our problems into disjoint sub-problems, so that the costs of solving the different subproblems can be added together without overestimating the cost of solving the original problem. Previously (Korf & Felner, 2002) we showed how to statically partition the sliding-tile puzzles into disjoint groups of tiles to compute an admissible heuristic, using the same partition for each state and problem instance. Here we extend the method and show that it applies to other domains as well. We also present another method for additive heuristics which we call dynamically partitioned pattern databases. Here we partition the problem into disjoint subproblems for each state of the search dynamically. We discuss the pros and cons of each of these methods and apply both methods to three different problem domains: the sliding-tile puzzles, the 4-peg Towers of Hanoi problem, and finding an optimal vertex cover of a graph. We find that in some problem domains, static partitioning is most effective. while in others dynamic partitioning is a better choice. In each of these problem domains, either statically partitioned or dynamically partitioned pattern database heuristics are the best known heuristics for the problem.

An Algorithm for Automatically Designing Deterministic Mechanisms without Payments

Abstract: Mechanism design is the art of designing the rules of the game so that a desirable outcome is reached even though the agents in the game behave selfishly. This is a difficult problem because the designer is uncertain about the agentsý preferences and the agents may lie about their preferences. Traditionally, the focus in mechanism design has been on designing mechanisms that are appropriate for a range of settings. While this approach has produced a number of famous mechanisms, much of the space of possible settings is still left uncovered. In contrast, in automated mechanism design (AMD), a mechanism is computed on the fly for the setting at hand - a universally applicable approach. In this paper we present (to our knowledge) the first algorithm designed specifically for AMD. It is designed for the special case where there is only one type-reporting agent, the mechanism must be deterministic, and payments are not possible. The algorithm relies on an association of a particular (easy to compute) mechanism to each subset of outcomes, and a proof that one such mechanism is an optimal one - which allows us to reduce the search space to one of size 2|O|. We propose an admissible heuristic to use in searching over this space, and show how it can be updated efficiently from node to node. We show how to apply branch and bound DFS as well as IDA* to this search space, and show that this approach outperforms CPLEX 8.0, a general-purpose solver, solidly on unstructured instances, both with and without an IR constraint. However, on our third type of instance, a bartering problem, CPLEX almost achieves the performance of our algorithm by exploiting the structure inherent in the domain.

Building adaptive tests using Bayesian networks

Abstract: We propose a framework for building decision strategies using Bayesian network models and discuss its application to adaptive testing. Dynamic programming and AO* algorithm are used to find optimal adaptive tests. The proposed AO* algorithm is based on a new admissible heuristic function.