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

http://tinf2.vub.ac.be/~dvermeir/courses/thesis/argumentation_frameworks.pdf

On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n -person games

Generative communication is the basis of a new distributed programming langauge that is intended for systems programming in distributed settings generally and on integrated network computers in particular. It differs from previous interprocess communication models in specifying that messages be added in tuple-structured form to the computation environment, where they exist as named, independent entities until some process chooses to receive them. Generative communication results in a number of distinguishing properties in the new language, Linda, that is built around it. Linda is fully distributed in space and distributed in time; it allows distributed sharing, continuation passing, and structured naming. We discuss these properties and their implications, then give a series of examples. Linda presents novel implementation problems that we discuss in Part II. We are particularly concerned with implementation of the dynamic global name space that the generative communication model requires.

Generative communication in Linda

In the Hamadryas baboon, males are substantially larger than females. A troop of baboons is subdivided into a number of ‘one-male groups’, consisting of one adult male and one or more females with their young. The male prevents any of ‘his’ females from moving too far from him. Kummer (1971) performed the following experiment. Two males, A and B, previously unknown to each other, were placed in a large enclosure. Male A was free to move about the enclosure, but male B was shut in a small cage, from which he could observe A but not interfere. A female, unknown to both males, was then placed in the enclosure. Within 20 minutes male A had persuaded the female to accept his ownership. Male B was then released into the open enclosure. Instead of challenging male A , B avoided any contact, accepting A’s ownership.

Evolution and the Theory of Games

The cultural origins of human cognition

Prolog is a simple but powerful programming language founded on symbolic logic. The basic computational mechanism is a pattern matching process (“unification”) operating on general record structures (“terms” of logic). We briefly review the language and compare it especially with pure Lisp. The remainder of the paper discusses techniques for implementing Prolog efficiently; in particular we describe how to compile the patterns involved in the matching process. These techniques are as incorporated in our DECsystem-10 Prolog compiler (written in Prolog). The code it generates is comparable in speed with that produced by existing DEC10 Lisp compilers. We argue that pattern matching is a better method for expressing operations on structured data than conventional selectors and constructors - both for the user and for the implementor.

Prolog - the language and its implementation compared with Lisp

In this paper we investigate updates of knowledge bases represented by logic programs. In order to represent negative information, we use generalized logic programs which allow default negation not only in rule bodies but also in their heads. We start by introducing the notion of an update P U of one logic program P by another logic program U. Subsequently, we provide a precise semantic characterization of P U , and study some basic properties of program updates. In particular, we show that our update programs generalize the notion of interpretation update. We then extend this notion to compositional sequences of logic programs updates P1 P2 ; defining a dynamic program update, and thereby introducing the paradigm of dynamic logic programming. This paradigm significantly facilitates modularization of logic programming, and thus modularization of non-monotonic reasoning as a whole. Specifically, suppose that we are given a set of logic program modules, each describing a diAerent state of our knowledge of the world. DiAerent states may represent diAerent time points or diAerent sets of priorities or perhaps even diAerent viewpoints. Consequently, program modules may contain mutually contradictory as well as overlapping information. The role of the dynamic program update is to employ the mutual relationships existing between diAerent modules to precisely determine, at any given module composition stage, the declarative as well as the procedural semantics of the combined program resulting from the modules. ” 2000 Elsevier Science Inc. All rights reserved.

Dynamic updates of non-monotonic knowledge bases

Syntax for Variable Binders: An Overview . . . . . . . . . . . . . . . . . . . . 239 Dale Miller Goal-Directed Proof Search in Multiple-Conclusioned Intuitionistic Logic . . 254 James Harland, Tatjana Lutovac, and Michael Winikoff Efficient EM Learning with Tabulation for Parameterized Logic Programs . 269 Yoshitaka Kameya and Taisuke Sato Model Generation Theorem Proving with Finite Interval Constraints . . . . . 285 Reiner Hähnle, Ryuzo Hasegawa, and Yasuyuki Shirai Combining Mobile Processes and Declarative Programming . . . . . . . . . . . . . 300 Rachid Echahed and Wendelin Serwe

Computational Logic — CL 2000

/pdf/well-founded-semantics-for-logic-programs-with-explicit-3ocz7t4jtc.pdf

Well founded semantics for logic programs with explicit negation

In this paper we present a new approach to evaluate indicative conditionals with respect to some background information specified by a logic program. Because the weak completion of a logic program admits a least model under the three-valued Lukasiewicz semantics and this semantics has been successfully applied to other human reasoning tasks, conditionals are evaluated under these least L-models. If such a model maps the condition of a conditional to unknown, then abduction and revision are applied in order to satisfy the condition. Different strategies in applying abduction and revision might lead to different evaluations of a given conditional. Based on these findings we outline an experiment to better understand how humans handle those cases. 1 Indicative Conditionals Conditionals are statements of the form if condition then consequence. In the literature the condition is also called if part, if clause or protasis, whereas the consequence is called then part, then clause or apodosis. Conditions as well as consequences are assumed to be finite sets (or conjunctions) of ground literals. Indicative conditionals are conditionals whose condition may or may not be true and, consequently, whose consequence also may or may not be true; however, the consequence is asserted to be true if the condition is true. Examples for indicative conditionals are the following: If it is raining, then he is inside. (1) If Kennedy is dead and Oswald did not shoot him, then someone else did. (2) If rifleman A did not shoot, then the prisoner is alive. (3) If the prisoner is alive, then the captain did not signal. (4) If rifleman A shot, then rifleman B shot as well. (5) If the captain gave no signal and rifleman A decides to shoot, then the prisoner will die and rifleman B will not shoot. (6) Conditionals may or may not be true in a given scenario. For example, if we are told that a particular person is living in a prison cell, then most people are ? The authors are mentioned in alphabetical order.

/pdf/on-indicative-conditionals-5f223kmxhx.pdf

Luís Moniz Pereira

Papers

Prolog - the language and its implementation compared with Lisp

Dynamic updates of non-monotonic knowledge bases

Computational Logic — CL 2000

Well founded semantics for logic programs with explicit negation

On Indicative Conditionals.