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

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

1.. HARD CASES 5. Legal Rights A. Legislation . . . We might therefore do well to consider how a philosophical judge might develop, in appropriate cases, theories of what legislative purpose and legal principles require. We shall find that he would construct these theories in the same manner as a philosophical referee would construct the character of a game. I have invented, for this purpose, a lawyer of superhuman skill, learning, patience and acumen, whom I shall call Hercules. I suppose that Hercules is a judge in some representative American jurisdiction. I assume that he accepts the main uncontroversial constitutive and regulative rules of the law in his jurisdiction. He accepts, that is, that statutes have the general power to create and extinguish legal rights, and that judges have the general duty to follow earlier decisions of their court or higher courts whose rationale, as l

Taking Rights Seriously

This introduction presents the main motivations for the development of Description Logics (DLs) as a formalism for representing knowledge, as well as some important basic notions underlying all systems that have been created in the DL tradition. In addition, we provide the reader with an overview of the entire book and some guidelines for reading it. We first address the relationship between Description Logics and earlier semantic network and frame systems, which represent the original heritage of the field. We delve into some of the key problems encountered with the older efforts. Subsequently, we introduce the basic features of DL languages and related reasoning techniques. DL languages are then viewed as the core of knowledge representation systems, considering both the structure of a DL knowledge base and its associated reasoning services. The development of some implemented knowledge representation systems based on Description Logics and the first applications built with such systems are then reviewed. Finally, we address the relationship of Description Logics to other fields of Computer Science.We also discuss some extensions of the basic representation language machinery; these include features proposed for incorporation in the formalism that originally arose in implemented systems, and features proposed to cope with the needs of certain application domains.

https://assets.cambridge.org/97805218/76254/frontmatter/9780521876254_frontmatter.pdf

The Description Logic Handbook

Scholars of culture, humanities and social sciences have increasingly come to an appreciation of the importance of the legal domain in social life, while critically engaged socio-legal scholars around the world have taken up the task of understanding "Law's Empire" in all of its cultural, political, and economic dimensions. The questions arising from these intersections, and addressing imperialisms past and present forms the subject matter of a special symposium issue of Social Identities under the editorship of Griffith University's Rob McQueen, and UBC's Wes Pue and with contributions from McQueen, Ian Duncanson, Renisa Mawani, David Williams, Emma Cunliffe, Chidi Oguamanam, W. Wesley Pue, Fatou Camara, and Dianne Kirkby. This paper introduces the volume, forthcoming in late 2007. The central problematique of this issue has previously been explored through the 2005 Law's Empire conference, an informal but vibrant postcolonial legal studies network.

Law's Empire

1. OVERVIEW 2. INDETERMINISM AND AGENCY 2.1 Branching time 2.1.1 Frames and Models 2.1.2 2.1.2 Propositions 2.2 Individual agency 2.2.1 Agents and choices 2.2.2 Stit operators 2.2.3 Some logical considerations 2.3 Individual ability 2.3.1 Kenny's objections 2.3.2 Brown's theory 2.3.3 Refraining and ability 2.4 Group agency and ability 2.4.1 Group actions 2.4.2 A group agency operator 3. OUGHT TO BE 3.1 The standard theory 3.2 A utilitarian theory 3.2.1 General models 3.2.2 Utilitarian models 3.2.3 Logic of the utilitarian ought 3.3 The Meinong/Chisholm analysis 3.3.1 The analysis 3.3.2 Some logical features 3.4 Evaluating the analysis 3.4.1 Agency in the complement 3.4.2 The gambling problem 4. OUGHT TO DO 4.1 Dominance 4.1.1 Ordering the propositions 4.1.2 A sure-thing argument 4.1.3 Ordering the actions 4.2 Dominance act utilitarianism 4.2.1 Optimal actions 4.2.2 The finite choice condition 4.3 A new deontic operator 4.3.1 The definition 4.3.2 Deontic logic and act utilitarianism 4.3.3 Logic of the dominance ought 4.4 Independence 4.4.1 Independence and conditionals 4.4.2 Conditionals and sure-thing reasoning 4.4.3 Refining the analysis 5. CONDITIONAL OUGHTS 5.1 Conditionally optimal actions 5.2 A conditional operator 5.2.1 The definition 5.2.2 Some logical considerations 5.3 Two patterns of argument 5.3.1 The action argument 5.3.2 The ought argument 5.4 Orthodox act utilitarianism 5.4.1 An example 5.4.2 The definition 5.4.3 An orthodox deontic operator 6. GROUP OUGHTS 6.1 Optimal group actions 6.2 Individual and group act utilitarianism 6.3 Deontic operators for group oughts 6.3.1 Definitions 6.3.2 Some logical points 6.4 Rule utilitarianism 6.4.1 Formulating the theory 6.4.2 Act and rule utilitarianism 7. STRATEGIC OUGHTS 7.1 Strategies 7.1.1 Basic idea 7.1.2 Limiting the range 7.2 Strategies and choices 7.2.1 Agency 7.2.2 Ability 7.3 Strategic dominance and optimality 7.3.1 Dominance 7.3.2 Optimality 7.4 A strategic ought operator 7.4.1 The definition 7.4.2 Logical points 7.4.3 Actualism and possibilism A. Proofs of validities and propositions A.1 Validities A.2 Propositions Bibliography Index

Agency and Deontic Logic

Early attempts at combining multiple inheritance with nonmonotonic reasoning were based on straight forward extensions of tree-structured inheritance systems, and were theoretically unsound. In The Mathematics of Inheritance Systems, or TMOIS, Touretzky described two problems these systems cannot handle: reasoning in the presence of true but redundant assertions, and coping with ambiguity. TMOIS provided a definition and analysis of a theoretically sound multiple inheritance system, accompanied by inference algorithms. Other definitions for inheritance have since been proposed that are equally sound and intuitive, but do not always agree with TMOIS. At the heart of the controversy is a clash of intuitions about certain fundamental issues such as skepticism versus credulity, the direction in which inheritance paths are extended, and classical versus intuitive notions of consistency. Just as there are alternative logics, there may be no single "best" approach to nonmonotonic multiple inheritance.

/pdf/a-clash-of-intuitions-the-current-state-of-nonmonotonic-4ybekrqnxz.pdf

A clash of intuitions: the current state of nonmonotonic multiple inheritance systems

This paper describes a new approach to inheritance reasoning in semantic networks allowing for multiple inheritance with exceptions The approach leads to a definition of inheritance that is both theoretically sound and intuitively attractive: it yields unambiguous results applied to any acyclic semantic net, and these results conform to our own intuitions in the cases in which the intuitions themselves are firm and unambiguous Since, however, the definition provided here is based on an alternative, skeptical view of inheritance reasoning, it does not always agree with previous definitions when it is applied to nets about which our intuitions are unsettled, or in which different reasoning strategies could naturally be expected to yield distinct results

A skeptical theory of inheritance in nonmonotonic semantic networks

L'A. propose une alternative a la stit theory (selon laquelle l'agent sees to it that A) developpee par N. Belnap et M. Perloff en terme de logique de l'accomplissement. L'A. privilegie l'operateur modal de l'action elabore par F. von Kutschera et J. F. Horty dans le sens d'un stit deliberatif qui peut s'appliquer a la logique deontique

/pdf/the-deliberative-stit-a-study-of-action-omission-ability-and-27xd9er9to.pdf

The deliberative stit: A study of action, omission, ability, and obligation

Introduction I Default logic 1 A Primer on Default Logic 1.1 Basic concepts 1.1.1 Default rules 1.1.2 Priority relations 1.1.3 Theories and scenarios 1.2 Central definitions 1.2.1 Binding defaults 1.2.2 Proper scenarios and extensions 1.3 Extensions and conclusions 1.3.1 Theories with multiple extensions 1.3.2 Theories without extensions 2 From Defaults to Reasons 2.1 An austere theory of reasons 2.2 Developing the theory 2.2.1 Conflict, strength, and defeat 2.2.2 Reasons and enablers 2.2.3 Reason amalgamation II Deontic Logic 3 Reasons and Oughts 3.1 The two logics 3.1.1 Simple oughts 3.1.2 Conditional oughts 3.1.3 Some history 3.2 Properties of the logics 4 Moral Conflicts 4.1 Logical objections 4.1.1 Agglomeration 4.1.2 Other logical objections 4.2 Conceptual objections 4.3 Objections based on reasons as moral forces III Some Elaborations 5 Variable Priorities and Exclusion 5.1 Variable priority default theories 5.1.1 The definition 5.1.2 Some examples 5.2 Exclusionary default theories 5.2.1 The definition 5.2.2 Some examples 5.3 Discussion 5.3.1 Downward closure of exclusion 5.3.2 Exclusion by weaker defaults 5.3.3 Excluders, intensifiers, and attenuators 6 Particularism 6.1 Dancy's argument 6.2 Evaluating the argument 6.3 Discussion 6.3.1 Pragmatic considerations 6.3.2 Borrowing a book 6.3.3 Moderate particularism IV Some complications 7 Skepticism and Floating Conclusions 7.1 Floating conclusions 7.1.1 Arguments and paths 7.1.2 Two versions of skepticism 7.2 The problem with floating conclusions 7.2.1 An example 7.2.2 Objections to the example 7.3 Discussion 7.3.1 Other examples 7.3.2 Skepticism 8 Problems with Priorities 8.1 Refining the concept of defeat 8.2 Controlling the order of application 8.3 Discussion 8.3.1 Inappropriate equilibria 8.3.2 Other orderings 8.3.3 Reinstatement V Appendices A Notes on the default logics A.1 Proper scenarios A.2 Some observations on defeat A.3 Normal default theories B Notes on the deontic logics B.1 A comparison with van Fraassen's logic B.2 A comparison with standard deontic logic

John F. Horty

Papers

Agency and Deontic Logic

A clash of intuitions: the current state of nonmonotonic multiple inheritance systems

A skeptical theory of inheritance in nonmonotonic semantic networks

The deliberative stit: A study of action, omission, ability, and obligation

Reasons as Defaults