646781
Value iteration working with belief subset
Value iteration is a popular algorithm for solving POMDPs. However, it is inefficient in practice. The primary reason is that it needs to conduct value updates for all the belief states in the (continuous) belief space. In this paper, we study value iteration working with a subset of the belief space, i.e., it conducts value updates only for belief states in the subset. We present a way to select belief subset and describe an algorithm to conduct value iteration over the selected subset. The algorithm is attractive in that it works with belief subset but also retains the quality of the generated values. Given a POMDP, we show how to <i>a priori</i> determine whether the selected subset is a <i>proper</i> subset of belief space. If this is the case, the algorithm carries the advantages of representation in space and efficiency in time.
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
1539580
Exploring Remote Object Coherence in XMLWeb Services
Object-level coherence in distributed applications and systems has been studied extensively. Object coherence in platform-specific and tightly-coupled systems is achieved with binary serialization protocols to ensure data structures and object graphs are safely transmitted, manipulated, and stored. On the opposite side of the spectrum are platform-neutral Web services that embrace XML as a serialization protocol for building loosely coupled systems. The advantages of XML to connect heterogeneous systems are plenty, but rendering programming-language specific data structures and object graphs in text form incurs a performance hit and presents challenges for systems that require object coherence. Achieving the latter goal poses difficulties by a phenomenon that is sometimes referred to as the "impedance mismatch" between programming language data types and XML schema types. This paper examines the problem, debunks the O/X-mismatch controversy, and presents a mix of static/dynamic algorithms for accurate XML serialization. Experimental results show that the implementation in C/C++ is efficient and competitive to binary protocols. Application of the approach to other programming languages, such as Java, is also discussed
32644316
A feature oriented approach to modeling and reusing requirements of software product lines
Getting a proper set of reusable requirements is an important milestone for successful software product line (SPL) practice. But modeling SPL requirements is usually more complex and difficult than modeling requirements for individual applications because it often involves systematically exploring commonality and variation across a set of applications. This paper presents a feature-oriented approach to modeling and reusing SPL requirements. A framework of the feature model is first proposed from five aspects, namely, basic structure, variation representation mechanism, variation binding time, variation constraint mechanism and quality feature analysis. Then, a customization-based reusing method is suggested, and a feature-oriented domain modeling method (FODM) is presented, including a concrete form of the feature model and a modeling process for it. At the end, a case study of a real domain is used to validate the feature model framework and demonstrate FODM.
139970
Immune Clonal MO Algorithm for 0/1 Knapsack Problems
In this paper, we introduce a new multiobjective optimization (MO) algorithm to solve 0/1 knapsack problems using the immune clonal principle. This algorithm is termed Immune Clonal MO Algorithm (ICMOA). In ICMOA, the antibody population is split into the population of the nondominated antibodies and that of the dominated antibodied. Meanwhile, the nondominated antibodies are allowed to survive and to clone. A metric of Coverage of Two Sets is adopted for the problems. This quantitative metric is used for testing the convergence to the Pareto-optimal front. Simulation results on the 0/1 knapsack problems show that ICMOA, in most problems, is able to find much better spread of solutions and better convergence near the true Pareto-optimal front compared with SPEA, NSGA, NPGA and VEGA.
18832812
Depth-First vs. Best-First Search: New Results
Best-first search (BFS) expands the fewest nodes among all admissible algorithms using the same cost function, but typically requires exponential space. Depth-first search needs space only linear in the maximum search depth, but expands more nodes than BFS. Using a random tree, we analytically show that the expected number of nodes expanded by depth-first branch-and-bound (DFBnB) is no more than O(d ċ N), where d is the goal depth and N is the expected number of nodes expanded by BFS. We also show that DFBnB is asymptotically optimal when BFS runs in exponential time. We then consider how to select a linear-space search algorithm, from among DFBnB, iterative-deepening (ID) and recursive best first search (RBFS). Our experimental results indicate that DFBnB is preferable on problems that can be represented by bounded-depth trees and require exponential computation; and RBFS should be applied to problems that cannot be represented by bounded-depth trees, or problems that can be solved in polynomial time.
13820146
Efficient computation of vanishing points
Man-made environments possess a lot of regularities which simplify otherwise difficult pose estimation and visual reconstruction tasks. The constraints arising front parallel and orthogonal lines and planes can be efficiently exploited at various stages of vision processing pipeline. In this paper we propose an approach for estimation of vanishing points by exploiting the constraints of structured man-made environments, where the majority of lines is aligned with the principal orthogonal directions of the world coordinate frame. We combine efficient image processing techniques used in the line detection and initialization stage with simultaneous grouping and estimation of vanishing directions using expectation maximization (EM) algorithm. Since we assume an uncalibrated camera the estimated vanishing points can be used towards partial camera calibration and estimation of the relative orientation of the camera with respect to the scene. The presented approach is computationally efficient and has been verified extensively by experiments.