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.

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Chaff: engineering an efficient SAT solver

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

A theory of diagnosis from first principles

As space exploration missions grow increasingly complex and are required to operate over extended durations of time, there is an amplified demand for accurate autonomous health management systems. In response, model-based programming approaches have been developed as scalable solutions to autonomous health management. Previous model-based monitoring and diagnosis techniques, such as Livingstone, were demonstrated to successfully and efficiently track nominal and failure modes in an abundance of scenarios, but at the cost of making simplifying assumptions about the observation probability that can lead to erroneous diagnoses after extended operation. Extending on Best-First-Belief-StateEnumeration (BFSE), this paper presents a new mode estimation technique called Best-First Belief State Update (BFBSU) that eliminates the observation probability assumption. BFBSU uses the full two-stage HMM belief state update equations as its utility function, thus further increasing estimator accuracy, while maintaining the efficiency required for real-time monitoring and fault detection.

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A Tractable Approach to Probabilistically Accurate Mode Estimation

Current unmanned systems are typically tele-operated and manpower intensive relying on human operators and their decision-making capabilities to perform platform and mission tasks. We envision a future where unmanned platforms have greater decision making abilities and autonomous behaviors are the norm. For example, unmanned autonomous systems will be deployed as teams along with their human supervisor. The UAS will interact with the supervisor at a high level, then take on an expanded role in mission planning, resource allocation and route planning. Toward this end, we have developed a risk-based adjustable autonomy system with a task directed adaptive sensing technology concept to allow system autonomy operation at a level in which an operator has confidence of success. We test our idea in simulation for a natural disaster recovery task. We present experimental results verifying the utility of our technology.

Risk-based sensing in support of adjustable autonomy

This paper presents fast non-sampling based methods to assess the risk for trajectories of autonomous vehicles when probabilistic predictions of other agents’ futures are generated by deep neural networks (DNNs). The presented methods address a wide range of representations for uncertain predictions including both Gaussian and non-Gaussian mixture models to predict both agent positions and control inputs conditioned on the scene contexts. We show that the problem of risk assessment when Gaussian mixture models of agent positions are learned can be solved rapidly to arbitrary levels of accuracy with existing numerical methods. To address the problem of risk assessment for non-Gaussian mixture models of agent position, we propose finding upper bounds on risk using nonlinear Chebyshev’s Inequality and sums-of-squares programming; they are both of interest as the former is much faster while the latter can be arbitrarily tight. These approaches only require higher order statistical moments of agent positions to determine upper bounds on risk. To perform risk assessment when models are learned for agent control inputs as opposed to positions, we propagate the moments of uncertain control inputs through the nonlinear motion dynamics to obtain the exact moments of uncertain position over the planning horizon. To this end, we construct deterministic linear dynamical systems that govern the exact time evolution of the moments of uncertain position in the presence of uncertain control inputs. The presented methods are demonstrated on realistic predictions from DNNs trained on the Argoverse and CARLA datasets and are shown to be effective for rapidly assessing the probability of low probability events.

Fast nonlinear risk assessment for autonomous vehicles using learned conditional probabilistic models of agent futures

Constraint optimization underlies many problems in AI. We present a novel algorithm for finite domain constraint optimization that generalizes branch-and-bound search by reasoning about sets of assignments rather than individual assignments. Because in many practical cases, sets of assignments can be represented implicitly and compactly using symbolic techniques such as decision diagrams, the set-based algorithm can compute bounds faster than explicitly searching over individual assignments, while memory explosion can be avoided by limiting the size of the sets. Varying the size of the sets yields a family of algorithms that includes known search and inference algorithms as special cases. Furthermore, experiments on random problems indicate that the approach can lead to significant performance improvements.

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Bounded search and symbolic inference for constraint optimization

The present paper describes an embedded system architecture, based on genetic algorithms, aiming safety mission execution by Unmanned Aerial Vehicles (UAVs). A two-dimensional non-convex environment is considered since obstacle avoidance happens. The embedded system integrates the Mission Oriented Sensor Array (MOSA) and In-Flight Awareness (IFA) systems, where MOSA is responsible for mission accomplishment and IFA stands for flight safety. The features of MOSA and IFA are combined under a platform that applies promising genetic algorithm approaches from literature to reach their goals. First, the genetic algorithms performance running from the embedded system is compared against their performance on a personal computer architecture. Next, the proposed system is evaluated in a real-world scenario using Software-In-The-Loop (SITL) technique. The computational results showed that the embedded system provides reliable results.

Brian C. Williams

Papers

A Tractable Approach to Probabilistically Accurate Mode Estimation

Risk-based sensing in support of adjustable autonomy

Fast nonlinear risk assessment for autonomous vehicles using learned conditional probabilistic models of agent futures

Bounded search and symbolic inference for constraint optimization

An embedded system architecture based on genetic algorithms for mission and safety planning with UAV