From the Publisher:
Probabilistic Reasoning in Intelligent Systems is a complete andaccessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. The author distinguishes syntactic and semantic approaches to uncertaintyand offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognitionin short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information.
Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.

Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

An Integrated Agent Architecture for Software Defined Radio. Rapid-prototype cognitive radio, CR1, was developed to apply these.The modern software defined radio has been called the heart of a cognitive radio. Cognitive radio: an integrated agent architecture for software defined radio. Http:bwrc.eecs.berkeley.eduResearchMCMACR White paper final1.pdf. The cognitive radio, built on a software-defined radio, assumes. Radio: An Integrated Agent Architecture for Software Defined Radio, Ph.D. The need for software-defined radios is underlined and the most important notions used for such. Mitola III, Cognitive radio: an integrated agent architecture for software defined radio, Ph.D. This results in the set-theoretic ontology of radio knowledge defined in the. Cognitive Radio An Integrated Agent Architecture for Software.This article first briefly reviews the basic concepts about cognitive radio CR. Cognitive Radio-An Integrated Agent Architecture for Software Defined Radio. Cognitive Radio RHMZ 2007. Software-defined radio SDR idea 1. Cognitive radio: An integrated agent architecture for software.Cognitive Radio SOFTWARE DEFINED RADIO, AND ADAPTIVE WIRELESS SYSTEMS2 Cognitive Networks. 3 Joseph Mitola III, Cognitive Radio: An Integrated Agent Architecture for Software Defined Radio Stockholm.

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Cognitive Radio An Integrated Agent Architecture for Software Defined Radio

Boolean satisfiability is probably the most studied of the combinatorial optimization/search problems. Significant effort has been devoted to trying to provide practical solutions to this problem for problem instances encountered in a range of applications in electronic design automation (EDA), as well as in artificial intelligence (AI). This study has culminated in the development of several SAT packages, both proprietary and in the public domain (e.g. GRASP, SATO) which find significant use in both research and industry. Most existing complete solvers are variants of the Davis-Putnam (DP) search algorithm. In this paper we describe the development of a new complete solver, Chaff which achieves significant performance gains through careful engineering of all aspects of the search-especially a particularly efficient implementation of Boolean constraint propagation (BCP) and a novel low overhead decision strategy. Chaff has been able to obtain one to two orders of magnitude performance improvement on difficult SAT benchmarks in comparison with other solvers (DP or otherwise), including GRASP and SATO.

/pdf/chaff-engineering-an-efficient-sat-solver-2ima1ekzuu.pdf

Chaff: engineering an efficient SAT solver

https://cs.ru.nl/~peterl/teaching/KeR/Theorist/reiteraij87.pdf

A theory of diagnosis from first principles

Adjuvants add up: “ABCD” for post-procedural dental analgesia

/pdf/runtime-verification-of-stochastic-systems-44wvauybux.pdf

Runtime Verification of Stochastic Systems

INTRODUCTION
Valid measures of pain are helpful to adjust treatment plans in real time after total joint replacement (TJR). We enhanced the existing Defense and Veterans Pain Rating Scale (DVPRS) with items related to pain at rest and movement, in specific reference to operative and nonoperative joints, creating the TJR-DVPRS. This manuscript is presented to validate the modified survey instrument. The aims of this psychometric study were to evaluate (1) the latent structure of the TJR-DVPRS, (2) the relationships among the pain dimensions represented on the TJR-DVPRS and the reference-standard Short-Form McGill Pain Questionnaire (version 2, SF-MPQ-2), and (3) the responsiveness of these two measures before and after TJR.


MATERIALS AND METHODS
This report involves a secondary analysis of pain surveys from 135 veterans undergoing TJR at one center who participated in a randomized trial. The study was approved by the institutional review boards from participating institutions. The TJR-DVPRS and SF-MPQ-2 were completed preoperatively, during postoperative day 1, and 6 weeks post-surgery. Standard psychometric evaluations included correlations, principal component analysis, and internal consistency of survey items and subscales, using preoperative baseline data as a frame of reference. Responsiveness analysis included assessing both effect size and thresholds of clinically important change for survey subscales using data from all three time points.


RESULTS
Two reliable subscales were identified for the TJR-DVPRS, one including items about pain intensity and interference related to the operative joint (Cronbach's α = .809) and the other including two pain items on the nonoperative joint. Combining the subscales indicated a two-factor solution: The first valid factor entailed the four subscales of the SF-MPQ-2 and the TJR-DVPRS operative joint subscales, indicating that these subscales validly represent the same pain dimension. The second valid factor entailed the TJR-DVPRS subscale addressing the nonoperative joint. Responsiveness analysis following accepted psychometric methods demonstrated significant decreases in pain from the preoperative period to 6 weeks postoperatively for all subscales. The TJR-DVPRS and SF-MPQ-2 subscales were similarly responsive, except for the SF-MPQ-2 neuropathic and TJR-DVPRS nonoperative joint subscales, which were minimally responsive during the preoperative to 6-week period.


CONCLUSIONS
The TJR-DVPRS is valid for use among veterans undergoing TJR and poses significantly less respondent burden than does the SF-MPQ-2. The brevity and ease of use of the TJR-DVPRS make it a practical tool for use during surgical recovery to monitor pain intensity at rest and with movement in the operative joint, and to assess pain interference with activity, sleep, and mood. The TJR-DVPRS is at least as responsive as the SF-MPQ-2, but the SF-MPQ-2 neuropathic and TJR-DVPRS nonoperative joint subscales were minimally responsive. Limitations of this study include the small sample size, under-representation of women (which would be expected in the veteran population), and using only veterans. Future validations studies should include civilians and active military TJR patients.

Validity of the Modified Defense and Veterans Pain Rating Scale Supplemented With Items Germane to Total Joint Replacement: Secondary Analysis From a Randomized Trial.

Many practical applications of robotics require systems that can operate safely despite uncertainty. In the context of motion planning, two types of uncertainty are particularly important when planning safe robot trajectories. The first is environmental uncertainty -- uncertainty in the locations of nearby obstacles, stemming from sensor noise or (in the case of obstacles' future locations) prediction error. The second class of uncertainty is uncertainty in the robots own state, typically caused by tracking or estimation error. To achieve high levels of safety, it is necessary for robots to consider both of these sources of uncertainty. In this paper, we propose a risk-bounded trajectory optimization algorithm, known as Sequential Convex Optimization with Risk Optimization (SCORA), to solve chance-constrained motion planning problems despite both environmental uncertainty and tracking error. Through experiments in simulation, we demonstrate that SCORA significantly outperforms state-of-the-art risk-aware motion planners both in planning time and in the safety of the resulting trajectories.

Brian C. Williams

Papers

Adjuvants add up: “ABCD” for post-procedural dental analgesia

Runtime Verification of Stochastic Systems

Validity of the Modified Defense and Veterans Pain Rating Scale Supplemented With Items Germane to Total Joint Replacement: Secondary Analysis From a Randomized Trial.

Chance-Constrained Trajectory Optimization for High-DOF Robots in Uncertain Environments