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

https://www.inf.ed.ac.uk/teaching/courses/seoc/2005_2006/resources/statecharts.pdf

Statecharts: A visual formalism for complex systems

Preface Acknowledgements 1. Introduction 2. Interactive artefacts 3. Plans 4. Situated actions 5. Communicative resources 6. Case and methods 7. Human-machine communication 8. Conclusion References Indices.

Plans and situated actions: the problem of human-machine communication

Prior to the twentieth century, theories of knowledge were inherently perceptual. Since then, developments in logic, statis- tics, and programming languages have inspired amodal theories that rest on principles fundamentally different from those underlying perception. In addition, perceptual approaches have become widely viewed as untenable because they are assumed to implement record- ing systems, not conceptual systems. A perceptual theory of knowledge is developed here in the context of current cognitive science and neuroscience. During perceptual experience, association areas in the brain capture bottom-up patterns of activation in sensory-motor areas. Later, in a top-down manner, association areas partially reactivate sensory-motor areas to implement perceptual symbols. The stor- age and reactivation of perceptual symbols operates at the level of perceptual components - not at the level of holistic perceptual expe- riences. Through the use of selective attention, schematic representations of perceptual components are extracted from experience and stored in memory (e.g., individual memories of green, purr, hot). As memories of the same component become organized around a com- mon frame, they implement a simulator that produces limitless simulations of the component (e.g., simulations of purr). Not only do such simulators develop for aspects of sensory experience, they also develop for aspects of proprioception (e.g., lift, run) and introspec- tion (e.g., compare, memory, happy, hungry). Once established, these simulators implement a basic conceptual system that represents types, supports categorization, and produces categorical inferences. These simulators further support productivity, propositions, and ab- stract concepts, thereby implementing a fully functional conceptual system. Productivity results from integrating simulators combinato- rially and recursively to produce complex simulations. Propositions result from binding simulators to perceived individuals to represent type-token relations. Abstract concepts are grounded in complex simulations of combined physical and introspective events. Thus, a per- ceptual theory of knowledge can implement a fully functional conceptual system while avoiding problems associated with amodal sym- bol systems. Implications for cognition, neuroscience, evolution, development, and artificial intelligence are explored.

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Perceptual symbol systems.

Strategies of Discourse Comprehension

Understanding Computers and Cognition: A New Founda- tion for Design

The use of augmented transition network grammars for the analysis of natural language sentences is described Structure-building actions associated with the arcs of the grammar network allow for the reordering, restructuring, and copying of constituents necessary to produce deep-structure representations of the type normally obtained from a transformational analysis, and conditions on the arcs allow for a powerful selectivity which can rule out meaningless analyses and take advantage of semantic information to guide the parsing The advantages of this model for natural language analysis are discussed in detail and illustrated by examples An implementation of an experimental parsing system for transition network grammars is briefly described

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Transition network grammars for natural language analysis

What's in a link

Publisher Summary This chapter focuses on the theoretical underpinnings for semantic network representations. It also focuses on several issues, such as the meaning of semantics, the need for explicit understanding of the intended meanings for various types of arcs and links, the need for careful thought in choosing conventions for representing facts as assemblages of arcs and nodes, and several specific difficult problems in knowledge representation—especially problems of relative clauses and quantification. When the semantics of the notations are made clear, many of the techniques used in existing semantic networks are inadequate for representing knowledge in general. The chapter presents the logical inadequacies of almost all current network notations for representing quantified information and also discusses some of the disadvantages of a few logically adequate techniques.

/pdf/what-s-in-a-link-foundations-for-semantic-networks-pxe0tabx5f.pdf

What's in a Link: Foundations for Semantic Networks

The present invention relates to a method and apparatus for generating responses to queries to a document retrieval system. The system responds to a specific request for information by locating and ranking portions of text that may contain the information sought. It locates small relevant passages of text (called "hit passages") and ranks them according to an estimate of the degree to which they correspond to the information sought. The system minimizes the number of these hit passages that need to be examined before an information seeker has either found the desired information or can safely conclude that the information sought is not in the collection of texts. A relaxation ranking mechanism is provided to accommodate paraphrase variations that occur between the description of the information sought and the content of the text passages that may constitute suitable answers, by retrieving phrases that are dissimilar to the query phrase to different degrees according to a predefined set of rules, and penalizing the retrieved phrases based upon the degree of this dissimilarity, thus providing the user with a priority organized query hit list.

William A. Woods

Papers

Understanding Computers and Cognition: A New Founda- tion for Design

Transition network grammars for natural language analysis

What's in a link

What's in a Link: Foundations for Semantic Networks

Method and apparatus for generating query responses in a computer-based document retrieval system