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

This paper proposes a new market-based distributed planning algorithm for multi-agent systems under uncertainty, called MIRA (Marketbased Iterative Risk Allocation). In large coordination problems, from power grid management to multi-vehicle missions, multiple agents act collectively in order to optimize the performance of the system, while satisfying mission constraints. These optimal plans are particularly susceptible to risk when uncertainty is introduced. We present a distributed planning algorithm that minimizes the system cost while ensuring that the probability of violating mission constraints is below a user-specified level. We build upon the paradigm of risk allocation [13], in which the planner optimizes not only the sequence of actions, but also its allocation of risk among each constraint at each time step. We extend the concept of risk allocation to multi-agent systems by highlighting risk as a good that is traded in a computational market. The equilibrium price of risk that balances the supply and demand is found by an iterative price adjustment process called tâtonnement (also known as Walrasian auction). The simulation results demonstrate the efficiency and optimality of the proposed distributed planner.

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Risk Allocation for Multi-agent Systems using Tatonnement

As autonomous systems and robots are applied to more real world situations, they must reason about uncertainty when planning actions. Mission success oftentimes cannot be guaranteed and the planner must reason about the probability of failure. Unfortunately, computing a trajectory that satisfies mission goals while constraining the probability of failure is difficult because of the need to reason about complex, multidimensional probability distributions. Recent methods have seen success using chance-constrained, model-based planning. However, the majority of these methods can only handle simple environment and agent models. We argue that there are two main drawbacks of current approaches to goal-directed motion planning under uncertainty. First, current methods suffer from an inability to deal with expressive environment models such as 3D non-convex obstacles. Second, most planners rely on considerable simplifications when computing trajectory risk including approximating the agent’s dynamics, geometry, and uncertainty. In this article, we apply hybrid search to the risk-bound, goal-directed planning problem. The hybrid search consists of a region planner and a trajectory planner. The region planner makes discrete choices by reasoning about geometric regions that the autonomous agent should visit in order to accomplish its mission. In formulating the region planner, we propose landmark regions that help produce obstacle-free paths. The region planner passes paths through the environment to a trajectory planner; the task of the trajectory planner is to optimize trajectories that respect the agent’s dynamics and the user’s desired risk of mission failure. We discuss three approaches to modeling trajectory risk: a CDF-based approach, a sampling-based collocation method, and an algorithm named Shooting Method Monte Carlo. These models allow computation of trajectory risk with more complex environments, agent dynamics, geometries, and models of uncertainty than past approaches. A variety of 2D and 3D test cases are presented including a linear case, a Dubins car model, and an underwater autonomous vehicle. The method is shown to outperform other methods in terms of speed and utility of the solution. Additionally, the models of trajectory risk are shown to better approximate risk in simulation.

Motion Planning Under Uncertainty with Complex Agents and Environments via Hybrid Search

In this paper, we address the problem of uncertainty propagation through nonlinear stochastic dynamical systems More precisely, given a discrete-time continuous-state probabilistic nonlinear dynamical system, we aim at finding the sequence of the moments of the probability distributions of the system states up to any desired order over the given planning horizon Moments of uncertain states can be used in estimation, planning, control, and safety analysis of stochastic dynamical systems Existing approaches to address moment propagation problems provide approximate descriptions of the moments and are mainly limited to particular set of uncertainties, eg, Gaussian disturbances In this paper, to describe the moments of uncertain states, we introduce trigonometric and also mixed-trigonometric-polynomial moments Such moments allow us to obtain closed deterministic dynamical systems that describe the exact time evolution of the moments of uncertain states of an important class of autonomous and robotic systems including underwater, ground, and aerial vehicles, robotic arms and walking robots Such obtained deterministic dynamical systems can be used, in a receding horizon fashion, to propagate the uncertainties over the planning horizon in real-time To illustrate the performance of the proposed method, we benchmark our method against existing approaches including linear, unscented transformation, and sampling based uncertainty propagation methods that are widely used in estimation, prediction, planning, and control problems

Moment-Based Exact Uncertainty Propagation Through Nonlinear Stochastic Autonomous Systems.

We introduce a novel task execution capability that enhances the ability of in-situ crew members to function independently from Earth by enabling safe and efficient interaction with automated systems. This task execution capability provides the ability to (1) map goal-directed commands from humans into safe, compliant, automated actions, (2) quickly and safely respond to human commands and actions during task execution, and (3) specify complex motions through teaching by demonstration. Our results are applicable to future surface robotic systems, and we have demonstrated these capabilities on JPL's All-Terrain Hex-Limbed Extra-Terrestrial Explorer (ATHLETE) robot.

Compliant Task Execution and Learning for Safe Mixed-Initiative Human-Robot Operations

We collaborated with the Monterey Bay Aquarium Research Institute (MBARI) to improve the depth control algorithm used by the Dorado class autonomous underwater vehicle (AUV) in conducting bathymetric surveys and other remote sensing tasks. The algorithm enables better bottom following by planning for the depth profile that follows the desired depth best, while pulling up safely for bathymetry. The algorithm allows the AUV to operate closer to the sea floor and in more variable submarine conditions. Deployment tests demonstrated improved AUV performance in a bathymetrically complex area three miles off shore in Monterey Bay, and highlighted areas where further research and development can enhance AUV operation.

Brian C. Williams

Papers

Risk Allocation for Multi-agent Systems using Tatonnement

Motion Planning Under Uncertainty with Complex Agents and Environments via Hybrid Search

Moment-Based Exact Uncertainty Propagation Through Nonlinear Stochastic Autonomous Systems.

Compliant Task Execution and Learning for Safe Mixed-Initiative Human-Robot Operations

AUV bathymetric mapping depth planning for bottom following splice linear programming algorithm