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.

/pdf/cognitive-radio-an-integrated-agent-architecture-for-1v855bviue.pdf

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

Space missions have historically relied upon a large ground staff, numbering in the hundreds for complex missions, to maintain routine operations When an anomaly occurs, this small army of engineers attempts to identify and work around the problem A piloted Mars mission, with its multiyear duration, cost pressures, half-hour communication delays and two-week blackouts cannot be closely controlled by a battalion of engineers on Earth Flight crew involvement in routine system operations must also be minimized to maximize science return It also may be unrealistic to require the crew have the expertise in each mission subsystem needed to diagnose a system failure and effect a timely repair, as engineers did for Apollo 13 Enter model-based autonomy, which allows complex systems to autonomously maintain operation despite failures or anomalous conditions, contributing to safe, robust, and minimally supervised operation of spacecraft, life support, In Situ Resource Utilization (ISRU) and power systems Autonomous reasoning is central to the approach A reasoning algorithm uses a logical or mathematical model of a system to infer how to operate the system, diagnose failures and generate appropriate behavior to repair or reconfigure the system in response The 'plug and play' nature of the models enables low cost development of autonomy for multiple platforms Declarative, reusable models capture relevant aspects of the behavior of simple devices (eg valves or thrusters) Reasoning algorithms combine device models to create a model of the system-wide interactions and behavior of a complex, unique artifact such as a spacecraft Rather than requiring engineers to all possible interactions and failures at design time or perform analysis during the mission, the reasoning engine generates the appropriate response to the current situation, taking into account its system-wide knowledge, the current state, and even sensor failures or unexpected behavior

/pdf/model-based-autonomy-for-robust-mars-operations-4u6l0os9y0.pdf

Model Based Autonomy for Robust Mars Operations

/pdf/air-combat-strategy-using-approximate-dynamic-programming-1skqqatooy.pdf

Air Combat Strategy Using Approximate Dynamic Programming

Activity analysis is introduced as a means to strategically cut away subspaces of a design problem that can quickly be ruled out as suboptimal. This results in focused regions of the space in which additional symbolic or numerical analysis can take place. Activity analysis is derived from a qualitative abstraction of the Karush-Kuhn-Tucker conditions of optimality, used to partition an optimization problem into regions which are nonstationary and qualitatively stationary (qslationarv). Activity analysis draws from the fields of gradient-based optimization, conflict-based approaches of combinatorial satisfying search, and monotonicity analysis.

Activity analysis: simplifying optimal design problems through qualitative partitioning†

Human activity recognition is a crucial ingredient in safe and efficient human–robot collaboration. In this paper, we present a new model-based human activity recognition system called logical activity recognition system (LCARS). LCARS requires much less training data compared to learning-based works. Compared to other model-based works, LCARS requires minimal domain-specific modeling effort from users. The minimal modeling is for two reasons: i) we provide a systematic and intuitive way to encode domain knowledge for LCARS and ii) LCARS automatically constructs a probabilistic estimation model from the domain knowledge. Requiring minimal training data and modeling effort allows LCARS to be easily applicable to various scenarios. We verify this through simulations and experiments.

A Model-Based Human Activity Recognition for Human–Robot Collaboration

This paper presents a risk-sensitive model-based plan executive, p-Sulu, as well as its application to energy-efficient room temperature control of a smart residential building called Connected Sustainable Home. p-Sulu minimizes the use of heating and cooling by optimally controlling the incoming sunlight through a south-facing facade made of electrochromic glass windows, whose opacity can be changed. It also helps the residents to choose energy-efficient behaviors by making suggestions on their schedule, such as when to leave home and when to go to bed. Furthermore, in the presence of uncertainties in the solar radiation and the outdoor temperature, p-Sulu robustly operates within a user-specified upper bound on the probability of constraint violations. The resulting robust optimal scheduling and control problem is solved by iterative solution of mixed-integer linear programmings. We demonstrate by simulations that p-Sulu significantly reduces the energy consumption compared to existing control approaches, while successfully limiting the risk of failure to satisfy residents' requirements.

Brian C. Williams

Papers

Model Based Autonomy for Robust Mars Operations

Air Combat Strategy Using Approximate Dynamic Programming

Activity analysis: simplifying optimal design problems through qualitative partitioning†

A Model-Based Human Activity Recognition for Human–Robot Collaboration

Risk-sensitive plan execution for connected sustainable home