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 autonomous spacecraft and other robotic systems grow increasingly complex, there is a pressing need for capabilities that more accurately monitor and diagnose system state while maintaining reactivity. Mode estimation addresses this problem by reasoning over declarative models of the physical plant, represented as a factored variant of Hidden Markov Models (HMMs), called Probabilistic Concurrent Constraint Automata (PCCA). Previous mode estimation approaches track a set of most likely PCCA state trajectories, enumerating them in order of trajectory probability. Although Best-First Trajectory Enumeration (BFTE) is efficient, ignoring the additional trajectories that lead to the same state can significantly underestimate the true state probability and result in misdiagnosis. This paper introduces an innovative belief approximation technique, called Best-First Belief State Enumeration (BFBSE), that addresses this limitation by computing estimate probabilities directly from the HMM belief state update equations. Theoretical and empirical results show that BFBSE significantly increases estimator accuracy, uses less memory, and requires less computation time when enumerating a moderate number of estimates for the approximate belief state of subsystem sized models.

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Diagnosis as approximate belief state enumeration for probabilistic concurrent constraint automata

There is huge demand for robots to work alongside humans in heterogeneous teams. To achieve a high degree of fluidity, robots must be able to (1) recognize their human co-worker's intent, and (2) adapt to this intent accordingly, providing useful aid as a teammate. The literature to date has made great progress in these two areas -- recognition and adaptation -- but largely as separate research activities. In this work, we present a unified approach to these two problems, in which recognition and adaptation occur concurrently and holistically within the same framework. We introduce Pike, an executive for human-robot teams, that allows the robot to continuously and concurrently reason about what a human is doing as execution proceeds, as well as adapt appropriately. The result is a mixed-initiative execution where humans and robots interact fluidly to complete task goals.Key to our approach is our task model: a contingent, temporally-flexible team-plan with explicit choices for both the human and robot. This allows a single set of algorithms to find implicit constraints between sets of choices for the human and robot (as determined via causal link analysis and temporal reasoning), narrowing the possible decisions a rational human would take (hence achieving intent recognition) as well as the possible actions a robot could consistently take (hence achieving adaptation). Pike makes choices based on the preconditions of actions in the plan, temporal constraints, unanticipated disturbances, and choices made previously (by either agent).Innovations of this work include (1) a framework for concurrent intent recognition and adaptation for contingent, temporally-flexible plans, (2) the generalization of causal links for contingent, temporally-flexible plans along with related extraction algorithms, and (3) extensions to a state-of-the-art dynamic execution system to utilize these causal links for decision making.

Watching and Acting Together: Concurrent Plan Recognition and Adaptation for Human-Robot Teams

In order for an autonomous agent to successfully complete its mission, the agent must be able to quickly re-plan on the fly, as unforeseen events arise in the environment. This is enabled through the use of temporally flexible plans, which allow the agent to adapt to execution uncertainties, by not over committing to timing constraints, and through continuous planners, which are able to replan at any point when the current plan fails. To achieve both of these requirements, planners must have the ability to reason quickly about timing constraints.

We enable continuous, temporally flexible planning through a temporal consistency algorithm (ITC), which supports incremental consistency testing on a new type of disjunctive temporal constraint network, the Temporal Plan Network (TPN), and supports focused search through incremental conflict extraction. The ITC algorithm combines the speed of shortest-path algorithms known to network optimization with the spirit of incremental algorithms such as Incremental A* and those used within truth maintenance systems (TMS). Empirical studies of ITC applied to the Kirk temporal planner demonstrate an order of magnitude speed increase on cooperative air vehicle scenarios and on randomly generated plans.

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Enabling fast flexible planning through incremental temporal reasoning with conflict extraction

This paper studies the path planning for Unmanned Aerial Vehicles (UAVs) under critical situations, where the aircraft has to execute a hard landing. Such critical situations can be provoked by equipment failures or extreme environmental situations that demand the UAV to abort the mission running and to land the aircraft without risk for people, properties and itself. First, a mathematical formulation is introduced to describe this problem. A planner system is proposed based on a multi-population genetic algorithm and a greedy heuristic. Computational results are conducted over a large set of scenarios with different levels of difficulty. Also, some simulations are executed using FlightGear simulator to illustrate the UAV's behaviour when landing under different wind velocities. The results achieved indicate the greedy heuristic is able to define faster feasible landing paths, whose quality can be improved by the evolutionary approach always within a short computation time.

A Multi-population Genetic Algorithm for UAV Path Re-planning under Critical Situation

As embedded systems grow increasingly complex, there is a pressing need for diagnosing and monitoring capabilities that estimate the system state robustly. This paper is based on approaches that address the problem of robustness by reasoning over declarative models of the physical plant, represented as a variant of factored Hidden Markov Models, called Probabilistic Concurrent Constraint Automata. Prior work on Mode Estimation of PCCAs is based on a Best-First Trajectory Enumeration (BFTE) algorithm. Two algorithms have since made improvements to the BFTE algorithm: 1) the Best-First Belief State Update (BFBSU) algorithm has improved the accuracy of BFTE and 2) the MEXEC algorithm has introduced a polynomial-time bounded algorithm using a smooth deterministic decomposable negation normal form (sd-DNNF) representation.

This paper introduces a new DNNF-based Belief State Estimation (DBSE) algorithm that merges the polynomial time bound of the MEXEC algorithm with the accuracy of the BFBSU algorithm. This paper also presents an encoding of a PCCA as a CNF with probabilistic data, suitable for compilation into an sd-DNNF-based representation. The sd-DNNF representation supports computing k belief states from k previous belief states in the DBSE algorithm.

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Brian C. Williams

Papers

Diagnosis as approximate belief state enumeration for probabilistic concurrent constraint automata

Watching and Acting Together: Concurrent Plan Recognition and Adaptation for Human-Robot Teams

Enabling fast flexible planning through incremental temporal reasoning with conflict extraction

A Multi-population Genetic Algorithm for UAV Path Re-planning under Critical Situation

DNNF-based belief state estimation