2749219
A Method for Speeding Up Value Iteration in Partially Observable Markov Decision Processes
We present a technique for speeding up the convergence of value iteration for partially observable Markov decisions processes (POMDPs). The underlying idea is similar to that behind modified policy iteration for fully observable Markov decision processes (MDPs). The technique can be easily incorporated into any existing POMDP value iteration algorithms. Experiments have been conducted on several test problems with one POMDP value iteration algorithm called incremental pruning. We find that the technique can make incremental pruning run several orders of magnitude faster.
16189288
An Alternative Formulation of Dynamic-Programming Updates for POMDPs
15388288
Performance of Linear-Space Search Algorithms
Abstract Search algorithms that use space linear in the search depth are widely employed in practice to solve difficult problems optimally, such as planning and scheduling. In this paper, we study the average-case performance of linear-space search algorithms, including depth-first branch-and-bound (DFBnB), iterative-deepening (ID), and recursive best-first search (RBFS). To facilitate our analyses, we use a random tree T(b,d) that has mean branching factor b , depth d , and node costs that are the sum of the costs of the edges from the root to the nodes. We prove that the expected number of nodes expanded by DFBnB on a random tree is no more than bd times the expected number of nodes expanded by best-first search (BFS) on the same tree, which usually requires space that is exponential in depth d . We also show that DFBnB is asymptotically optimal when BFS runs in exponential time, and ID and RBFS are asymptotically optimal when the edge costs of T ( b , d ) are integers. If bp 0 is the expected number of children of a node whose costs are the same as that of their parent, then the expected number of nodes expanded by these three linear-space algorithms is exponential when bp o d 4 ) when bp 0 = 1, and at most quadratic when bp 0 > 1. In addition, we study the heuristic branching factor of T ( b , d ) and the effective branching factor of BFS, DFBnB, ID, and RBFS on T ( b , d ). Furthermore, we use our analytic results to explain a surprising anomaly in the performance of these algorithms, and to predict the existence of a complexity transition in the Asymmetric Traveling Salesman Problem.
76909
Heuristic search in artificial intelligence
In February of this year, Claude E. Shannon and Herbert A. Simon passed away, both at age 84. In his 1950 paper entitled, “Programming a Computer to Play Chess” [3], Shannon laid out the theory of minimax search with bounded lookahead and static heuristic evaluation, the basis of almost all subsequent work on two-player games. Along with Allen Newell, Herbert Simon established the idea of heuristic search in a problem space as one of the fundamental paradigms of problem solving in artificial intelligence [1]. We feel a deep sorrow for the loss of these two giants of our field. Both men are also known for their deep scientific contributions to other fields as well. Shannon was the father of information and communication theory, and Simon was a founding father of organizational behavior and the information processing model of cognitive psychology. Although Simon and Shannon are no longer with us, their work continues as a source of inspiration in virtually every field they touched. We dedicate this special issue on heuristic search in artificial intelligence to Claude Shannon and Herbert Simon. Heuristic search remains as a core area of artificial intelligence (AI). The use of a good search algorithm is often a critical factor in the performance of an intelligent system. As with most areas of AI, there has been steady progress in heuristic search research over the years. This progress can be measured by several different yardsticks, including finding optimal solutions to larger problems, making higher quality decisions in fixedsize problems, handling more complex domains including dynamic environments with incomplete and uncertain information, being able to analyze and predict the performance of heuristic search algorithms, and the increasing deployment of real-world applications of search algorithms. The success of the Deep Blue Chess machine in defeating Gary Kasparov, the human world champion, in an exhibition match in 1997 was a landmark in the field of AI, the achievement of a goal that had been pursued for almost 50 years. By coincidence, this special issue contains the same number of papers as the Artificial Intelligence Journal special issue on Search and Heuristics, published in 1983. What Judea
17059774
EM algorithms of Gaussian mixture model and hidden Markov model
The HMM (hidden Markov model) is a probabilistic model of the joint probability of a collection of random variables with both observations and states. The GMM (Gaussian mixture model) is a finite mixture probability distribution model. Although the two models have a close relationship, they are always discussed independently and separately. The EM (expectation-maximum) algorithm is a general method to improve the descent algorithm for finding the maximum likelihood estimation. The EM of HMM and the EM of GMM have similar formulae. Two points are proposed in this paper. One is that the EM of GMM can be regarded as a special EM of HMM. The other is that the EM algorithm of GMM based on symbols is faster in implementation than the EM algorithm of GMM based on samples (or on observation) traditionally.
27912320
Novel Preparation Scheme Of Monolithic Pyroelectric Thin Film Infrared Sensor
This paper presents a new preparation scheme of monolithic pyroelectric thin film infrared sensor. The sensor can be prepared directly on readout circuit and its electrode can be connected to the readout circuit through a via. PbTiO3 thin film is used as pyroelectric material. Porous SiO2 thin film is applied onto silicon wafer to improve the thermal isolation of the pyroelectric layer. Copper interconnection is used in readout circuit to solve the problem that aluminium interconnection can't endure the annealing temperature of PbTiO3 thin film.
16870249
Selecting Degenerate Multiplex PCR Primers
Single Nucleotide Polymorphism (SNP) Genotyping is an important molecular genetics technique in the early stages of producing results that will be useful in the medical field. One of the proposed methods for performing SNP Genotyping requires amplifying regions of DNA surrounding a large number of SNP loci. In order to automate a portion of this method and make the use of SNP Genotyping more widespread, it is important to select a set of primers for the experiment. Selecting these primers can be formulated as the Multiple Degenerate Primer Design (MDPD) problem. An iterative beam-search algorithm, Multiple, Iterative Primer Selector (MIPS), is presented for MDPD. Theoretical and experimental analyses show that this algorithm performs well compared to the limits of degenerate primer design and the number of spurious amplifications should be small. Furthermore, MIPS outperforms an existing algorithm which was designed for a related degenerate primer selection problem.