52828064
Smart-Aloha for Multi-Hop Wireless Networks

This paper presents a novel slotted ALOHA-based protocol for use in ad hoc networks where nodes are equipped with adaptive array smart antennas. The protocol relies on the ability of the antenna and DoA (Direction of Arrival) algorithms to identify the direction of transmitters and then beamform appropriately to maximize SINR (Signal to Interference and Noise Ratio) at the receiver. The performance of the protocol is evaluated using analytical modeling as well as detailed simulation in OPNET and Matlab where we demonstrate the benefits of using smart antennas. The impact of using different number of antenna elements is also studied for this environment.
18021791
Energy Consumption of TCP in Ad Hoc Networks
In this paper we study the energy cost (protocol processing and communication cost) and goodput of different flavors of TCP (Transmission Control Protocol) in ad hoc networks. We implemented a testbed and measured the actual energy cost as well as goodput of running TCP Reno, Newreno, SACK (Selective ACKnowledgement) and a version that combines Explicit Link Failure Notification (ELFN) [7] and Explicit Congestion Notification (ECN) [5] in Newreno. We see that the use of ECN & ELFN does yield higher goodput in most cases with a corresponding lower total energy cost. We see an energy savings of between 20% and 500% depending on the network conditions.
1844435
Neural network analysis of MINERVA scene analysis benchmark
Scene analysis is an important area of research with the aim of identifying objects and their relationships in natural scenes. The MINERVA benchmark has recently been introduced in this area for testing different image processing and classification schemes. We present results on the classification of eight natural objects in the complete set of 448 natural images using neural networks. An exhaustive set of experiments with this benchmark has been conducted using four different segmentation methods and five texture-based feature extraction methods. The results in this paper show the performance of a neural network classifier on a ten fold cross-validation task. On the basis of the results produced, we are able to rank how well different image segmentation algorithms are suited to the task of region of interest identification in these images, and we also see how well texture extraction algorithms rank on the basis of classification results.
8689480
Cobot: A Social Reinforcement Learning Agent
We report on the use of reinforcement learning with Cobot, a software agent residing in the well-known online community LambdaMOO. Our initial work on Cobot (Isbell et al.2000) provided him with the ability to collect social statistics and report them to users. Here we describe an application of RL allowing Cobot to take proactive actions in this complex social environment, and adapt behavior from multiple sources of human reward. After 5 months of training, and 3171 reward and punishment events from 254 different LambdaMOO users, Cobot learned nontrivial preferences for a number of users, modifing his behavior based on his current state. Here we describe LambdaMOO and the state and action spaces of Cobot, and report the statistical results of the learning experiment.
47063936
Predictive Linear-Gaussian Models of Stochastic Dynamical Systems
Models of dynamical systems based on predictive state representations (PSRs) are defined strictly in terms of observable quantities, in contrast with traditional models (such as Hidden Markov Models) that use latent variables or state-space representations. In addition, PSRs have an effectively infinite memory, allowing them to model some systems that finite memory-based models cannot. Thus far, PSR models have primarily been developed for domains with discrete observations. Here, we develop the Predictive Linear-Gaussian (PLG) model, a class of PSR models for domains with continuous observations. We show that PLG models subsume Linear Dynamical System models (also called Kalman filter models or state-space models) while using fewer parameters. We also introduce an algorithm to estimate PLG parameters from data, and contrast it with standard Expectation Maximization (EM) algorithms used to estimate Kalman filter parameters. We show that our algorithm is a consistent estimation procedure and present preliminary empirical results suggesting that our algorithm outperforms EM, particularly as the model dimension increases.
6543198
Reinforcement Learning for Spoken Dialogue Systems
Recently, a number of authors have proposed treating dialogue systems as Markov decision processes (MDPs). However, the practical application of MDP algorithms to dialogue systems faces a number of severe technical challenges. We have built a general software tool (RLDS, for Reinforcement Learning for Dialogue Systems) based on the MDP framework, and have applied it to dialogue corpora gathered from two dialogue systems built at AT&T Labs. Our experiments demonstrate that RLDS holds promise as a tool for "browsing" and understanding correlations in complex, temporally dependent dialogue corpora.
17852912
Lock Free Data Structures Using STM in Haskell
This paper explores the feasibility of re-expressing concurrent algorithms with explicit locks in terms of lock free code written using Haskell's implementation of software transactional memory. Experimental results are presented which show that for multi-processor systems the simpler lock free implementations offer superior performance when compared to their corresponding lock based implementations.