2770616
Learning Domain-Specific Control Knowledge from Random Walks
We describe and evaluate a system for learning domain-specific control knowledge. In particular, given a planning domain, the goal is to output a control policy that performs well on "long random walk" problem distributions. The system is based on viewing planning domains as very large Markov decision processes and then applying a recent variant of approximate policy iteration that is bootstrapped with a new technique based on random walks. We evaluate the system on the AIPS-2000 planning domains (among others) and show that often the learned policies perform well on problems drawn from the long–random-walk distribution. In addition, we show that these policies often perform well on the original problem distributions from the domains involved. Our evaluation also uncovers limitations of our current system that point to future challenges.
477360
Approximate Policy Iteration with a Policy Language Bias
We explore approximate policy iteration, replacing the usual cost-function learning step with a learning step in policy space. We give policy-language biases that enable solution of very large relational Markov decision processes (MDPs) that no previous technique can solve. In particular, we induce high-quality domain-specific planners for classical planning domains (both deterministic and stochastic variants) by solving such domains as extremely large MDPs.
14200816
Context-dependent tree-structured image classification using the QDA distortion measure and the hidden Markov model
Vector quantization based on the Gauss mixture model (GMM) and the quadratic discriminant analysis (QDA) distortion measure has been shown to perform well in statistical image classification problems. Previous work in this area has concentrated on designing a separate GMM-based vector quantizer using the QDA distortion measure for each class using full search. We design a single vector quantizer for all classes using a tree-structured algorithm based on the (generalized) BFOS algorithm. This reduces the search complexity, while it increases the correct classification rate. Further, the pruning stage of our algorithm takes into account the dependencies between the image blocks assuming a hidden Markov model (HMM). During the test stage, our algorithm aims to iteratively maximize the joint probability of occurrence of all image blocks based on the HMM. Our simulation results indicate that our algorithm performs better (both in terms of computational complexity and classification rate) when compared to the previously published algorithms based on the GMM.
6861234
Vector quantization with model selection
We propose an iterative algorithm that incorporates model selection into entropy-constrained vector quantization. Two model selection steps are added to the classic Lloyd algorithm as additional necessary conditions for optimality. Codewords are pruned by using a Lagrangian with entropy and codebook size constraints. Relevant features are found by using a partitioned vector quantization. Relevant and irrelevant features are modelled independently. Moreover, we model irrelevant features by a global probability density function to make them independent of partition cells. This enables us to avoid a problem in comparing the performances of vector quantizers in different dimensional spaces. As a Lagrangian decreases, we not only obtain a locally optimal codebook, but also reduce codebook size and identify relevant features.
11748055
A New Search Algorithm using Autonomous and Cooperative Multiple Sensor Nodes
In this paper, we study search algorithms for using a set of autonomous and cooperative mobile sensor nodes (MSN) with limited sensing and communication ranges to search a large area. Our objectives include minimizing the total search time and the travel distance of MSNs while enabling fault tolerance to possible MSN failures. We propose a new rendezvous scheme, namely X synchronization (XS) to facilitate the exchange of both data and control signals among the MSNs during search. We also devise a way to calculate appropriate timeout periods used to detect an MSN failure at rendezvous points and describe how surviving MSNs subsequently carry out the search mission. Numerical analysis and simulations have been performed to evaluate the performance of XS. The results show that XS can outperform other rendezvous schemes in terms of the total search time and the average travel distance of MSNs.
6584448
Modeling and deformation of arms and legs based on ellipsoidal sweeping
We present a new approach to the modeling and deformation of a human or virtual character's arm and legs. Each limb is represented as a set of ellipsoids of varying size interpolated along a skeleton curve. A base surface is generated by approximating these ellipsoids with a swept ellipse, and the difference between that and the detailed shape of the arm or leg is represented as a displacement map. We demonstrate that the natural bending of arms and legs can be emulated using this approach, and show its effectiveness by articulating the limbs of a scanned human body and those of a virtual character.
3134846
Tiny-sync: Tight time synchronization for wireless sensor networks
Time synchronization is a fundamental middleware service for any distributed system. Wireless sensor networks make extensive use of synchronized time in many contexts (e.g., data fusion, TDMA schedules, synchronized sleep periods, etc.). We propose a time synchronization method relevant for wireless sensor networks. The solution features minimal complexity in network bandwidth, storage as well as processing, and can achieve good accuracy. Especially relevant for sensor networks, it also provides tight, deterministic bounds on offset and clock drift. A method for synchronizing the entire network is presented. The performance of the algorithm is analyzed theoretically and validated on a realistic testbed. The results show that the proposed algorithm outperforms existing algorithms in terms of precision and resource requirements.