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

Agent behavior is arguably the greatest source of uncertainty in trajectory planning for autonomous vehicles. This problem has motivated significant amounts of work in the behavior prediction community on learning rich distributions of the future states and actions of agents. However, most current work in trajectory planning in the presence of uncertain agents or obstacles is limited to the case of Gaussian uncertainty, which is a limited representation, or requires sampling, which can be computationally intractable to encode in an optimization problem. In this paper, we present a general method for enforcing chance-constraints on the probability of collision with other agents in model predictive control problems for autonomous driving that can be used with non-Gaussian mixture models of agent positions. Our method involves using statistical moments of the non-Gaussian distributions to enforce Cantelli's Inequality, which we show can upper bound the probability of collision. We then demonstrate its application to chance-constrained trajectory planning using model predictive contouring control. In experiments, we show that the resulting optimization problem can be solved with state-of-the-art nonlinear program (NLP) solvers to plan trajectories with 5 second horizons in real-time.

Non-Gaussian Chance-Constrained Trajectory Planning for Autonomous Vehicles in the Presence of Uncertain Agents.

Simple Temporal Networks with Uncertainty provide a useful framework for modeling temporal constraints and, importantly, for modeling actions with uncertain durations. To determine whether we can construct a schedule for a given network, we typically consider one of two types of controllability: dynamic or strong. These controllability checks have strict conditions on how uncertainty is resolved; uncertain outcomes are either recognized immediately or not at all. In this paper, we introduce delay controllability, a novel generalization of both strong and dynamic controllability that additionally exposes a large range of controllability classes in between. To do so, we use a delay function γ to parameterize our controllability checking. This delay function represents the difference between when an event happens and the time that it is observed. We also provide a single unified algorithm for checking delay controllability that runs in O(n) time, matching the best known runtime for dynamic controllability, which we use to motivate the decision to generalize dynamic and strong controllability. We conclude by providing an empirical evaluation of delay controllability, demonstrating its superior accuracy and practical efficiency as compared to other existing approximations.

Delay Controllability: Multi-Agent Coordination under Communication Delay

Current network approaches aim to maximize network utilization when routing flows. While such approaches are fast and usually result in acceptable behavior, existing methods are not mission aware. There is no concept of utility maximization, no capability to handle flows with specified deadlines and loss requirements, and no guarantees over the probability of network saturation. In the presence of network degradation due to attacks, there is no guarantee that important flows will be properly transported. In this paper, we present RADMAX: a system for Risk And Deadline Aware Planning for Maximum Utility based on constraint programming, which allows us to handle higher level mission specifications. We show the correctness of RADMAX with respect to loss and delay bounds, provide results for the optimality of RADMAX with respect to the mission utility, and review current results on computational performance.

RADMAX: Risk and Deadline Aware Planning for Maximum Utility.

Chance Constrained Markov Decision Processes maximize reward subject to a bounded probability of failure, and have been frequently applied for planning with potentially dangerous outcomes or unknown environments. Solution algorithms have required strong heuristics or have been limited to relatively small problems with up to millions of states, because the optimal action to take from a given state depends on the probability of failure in the rest of the policy, leading to a coupled problem that is difficult to solve. In this paper we examine a generalization of a CCMDP that trades off probability of failure against reward through a functional relationship. We derive a constraint that can be applied to each state history in a policy individually, and which guarantees that the chance constraint will be satisfied. The approach decouples states in the CCMDP, so that large problems can be solved efficiently. We then introduce Vulcan, which uses our constraint in order to apply Monte Carlo Tree Search to CCMDPs. Vulcan can be applied to problems where it is unfeasible to generate the entire state space, and policies must be returned in an anytime manner. We show that Vulcan and its variants run tens to hundreds of times faster than linear programming methods, and over ten times faster than heuristic based methods, all without the need for a heuristic, and returning solutions with a mean suboptimality on the order of a few percent. Finally, we use Vulcan to solve for a chance constrained policy in a CCMDP with over $10^{13}$ states in 3 minutes.

Brian C. Williams

Papers

Non-Gaussian Chance-Constrained Trajectory Planning for Autonomous Vehicles in the Presence of Uncertain Agents.

Delay Controllability: Multi-Agent Coordination under Communication Delay

RADMAX: Risk and Deadline Aware Planning for Maximum Utility.

Vulcan: A Monte Carlo Algorithm for Large Chance Constrained MDPs with Risk Bounding Functions.

Risk-aware Planning in Hybrid Domains: An Application to Autonomous Planetary Rovers