bluuebunny/crossref_metadata_2025_split huggingface dataset:
Extending SLD with finite failure
Authors: Hogger Christopher John 
Published: November 2023

Abstract
Although the SLD paradigm is theoretically adequate for all computational purposes, the pure Horn-clause language on which it is based is unlikely to become the dominant logic-programming language of the future. In fact it never has been the mainstream logic-programming language, as every Prolog programmer will testify-writing a logic program for a real-world application nearly always seems to require the expression of some intention or other for which there is apparently no practical representation in pure Horn-clause logic. It is futile to resist this fact of computational life; in the end, the cumulative and collective experience of serious practitioners has to be respected.
The designers of Prolog anticipated this and consequently felt obliged to add many non-logical extensions to the Horn-clause kernel. In doing so they placed greater weight upon the concrete needs of the practitioners than upon the more abstract desires of the theoreticians. A lot of research effort has since been devoted to the problem of rendering those extensions either semantically intelligible or methodologically redundant. A substantial part of that effort has concentrated upon one particular issue-the finite failure extension.
Special applications of SLDNF
Authors: Hogger Christopher John 
Published: November 2023

Abstract
The SLDNF inference system has come to be accepted as a staple feature of the logic programming paradigm. It is perhaps in recognition of this that programs and queries containing fail calls are now commonly referred to as normal programs and normal queries-they are what you would normally expect to use in practical applications. Up to a point, Prolog employs SLDNF in order to implement its ‘not’ primitive, which is merely fail by another name. Most versions of Prolog do not, however, enforce the safety requirement, leaving this responsibility instead to the programmer.
Given the semantic complications attending SLDNF, could we make do without it and simply stick to SLD? In theory we could do so, since the SLD paradigm is, in principle, capable of computing everything that is computable. But in practice we would pay a heavy price-our programs would be less concise and their executions less efficient. Consider, for example, the problem of showing positively that the element 3 is not a member of the set { 1, 2}. Without the use of fail we are obliged to write a program computing a relation ‘not_mem’ relying, ultimately, upon some built-in inequality primitive -/-. Moreover, sets will then have to be represented by structured terms and searched by explicitly-defined recursive procedures:
Program verification
Authors: Hogger Christopher John 
Published: November 2023

Abstract
We have seen that deduction can be applied to logic programs in order either to compute answers to queries (by total evaluation) or to derive new programs (by partial evaluation). In this Theme we look at the use of deduction for showing that programs are correct relative to logic specifications. In this context correctness is taken to be the property whereby, in relation to any query, the answers logically determined by the progam are exactly those logically determined by the specification.
A logic specification is a set S of logic sentences, asserted independently of the program, which defines all the relations named in that program. In general it is both natural and sufficient for each sentence in S defining a relation R to take the propositional form
Use of Sequential Bayes with Class Probability Trees
Authors: Michie D , Attar A Al 
Published: November 2023

Abstract
For building classifiers from data, rule induction has established itself as an alternative to multivariate statistical approaches, including those of ‘neural’ computing. But modern rule-induction algorithms such as CART and C4 have not yet found a fully satisfactory way of discriminating logical from statistical forms of complexity in data. Systems of sequential Bayes rules offer a less ad hoc basis for combining probabilistic with rule-based approaches. In Evidencer, tree-structured rules are linked in a PROSPECTOR-like procedural hierarchy, and updated and processed according to a thresholding regime adapted from Wald’s sequential analysis (1947). The resulting formalism steers a course between the oversimplifications of PROSPECTOR and the complexity of full multi-level Bayes, while retaining precise error bounds on decisions.
Computability
Authors: Hankin Chris 
Published: November 2023

Abstract
A classical application of the A-calculus was in the study of computability. This is the topic of this chapter. Since most computations involve repetitive execution of “code”, fixed points (for encoding recursion) play a fundamental role; we start by reviewing the Fixed Point Theorem and (re-)introducing the concept of a fixed point combinator. Next, rather than add delta rules for constants (see Chapter 3), we explore how constants can be handled in the pure calculus. We introduce several ways of encoding numerals and functions on them. In the third section, we introduce the notion of A-definability- this allows us to relate the A-calculus to other formalisms such as Kleene’s Recursive Functions and Turing Machines. The final section of this chapter discusses the issue of decidability in the A-calculus and we present some undecidable problems.
Introduction
Authors: Laidler Keith J 
Published: November 2023

Abstract
This chapter outlines the problem area which the material in this book addresses, namely, the application of quantitative methods–popularly known as software metrics – to software engineering and the design of system architectures or structures in particular. It is argued that this is of considerable significance because of the potential impact of poor design decisions upon a wide range of quality factors of a system when it is finally implemented. The software engineering background to the book is then described. The chapter concludes with an outline of the structure of the book and a summary of its key points. This book is concerned with quantitative approaches to software engineering, otherwise known as software metrics. Within this rather broad domain the book addresses a particular concern: the production of software system designs. To date, the main thrust of research in software metrics has been towards the measurement of code, yet it is our belief that by the time the code is available for measurement, the majority of a software project’s resources have been committed, so that any strategic changes in direction become prohibitively expensive.
Summary
Authors: Laidler Keith J 
Published: November 2023

Abstract
Here we review the scope of the work described within this book, and summarize its major findings. We proceed to discuss some of the weaknesses of this research and highlight the areas of potential significance which have been uncovered but which have yet to be explored. To recap: this book has been concerned with the development and, even more important, the evaluation of a set of system architecture metrics that might provide the software designer with feedback while making design decisions. This aim has, in turn, generated another aim, which has been the derivation of a method to support the development and evaluation of software metrics in general.
A Generalization of the Least General Generalization
Authors: Arimura H , Shinohara T , Otsuki S , Ishizaka H 
Published: November 2023

Abstract
In this chapter, we present a polynomial time algorithm, called a k-minimal multiple generalization (k-mmg) algorithm, where k ≥ 1, and its application to inductive learning problems. The algorithm is a natural extension of the least general generalization algorithm developed by Plotkin and Reynolds. Given a finite set of ground first-order terms, the k-mmg algorithm generalizes the examples by at most k first-order terms, while Plotkin’s algorithm does so by a single first-order term. We apply the k-mmg algorithm to several learning problems in inductive logic programming, and knowledge discovery in databases.
More Than Meets the Eye: Animal Learning and Knowledge Induction
Authors: Kehoe E J 
Published: November 2023

Abstract
Animals share many of the same learning problems as faced by untutored machine systems. In the ordinary environment, animals must continuously discover associations between potential signals and subsequent events that are crucial to their wellbeing. Moreover, the animals must build this world model from a highly limited training set, without having prior knowledge or verbal instructions. Experiments using conditioned reflex methods have examined issues fundamental to all learning systems. These include: • Credit assignment, e.g., animals capitalize on any sequence of reliable signals for subsequent events. • Representation, e.g., the XOR problem can be solved by animals. • Productivity, e.g., the rate of learning improves across successive problems that are structurally similar but differ in their superficial features. • Anticipation, e.g., conditioned responses are finely timed so that they are neither too early nor too late during a warning signal.
Recent Progress with BOXES
Authors: Sammut C 
Published: November 2023

Abstract
The Boxes algorithm of Michie and Chambers (1968) has proved to be an effective and flexible method for learning to control dynamic systems. The algorithm, in its original form has been used as a benchmark for many experiments in control tasks such as pole balancing. Recent work in our laboratory has shown that the Boxes algorithm can be improved to yield very good learning rates. We describe experiments on a variety of update functions and discuss their robustness. We also develop the notion of freezing of Boxes, suggested by Miehe and implemented by Bain (1990). We have also been concerned with synthesising a readable account of the control strategy employed by a set of boxes. Some preliminary work has begun in combining decision tree Learning algorithms with Boxes. Using this method, we regard BOXES as the acquirer of sub-cognitive skills and the decision tree induction as a means of introspecting on the learned strategy to generate understandable control rules.
Inductively Speeding Up Logic Programs
Authors: Numao M , Maruoka T , Shimura M 
Published: November 2023

Abstract
This chapter presents a speed-up learning method for logic programs, which accelerates a program by composing macro clauses based on partial structures of proof trees. Many systems have been proposed for composing useful macros, e.g., some of them select macros that connect two peaks in a heuristic function. Another employs heuristics that select useful macros. Although they work well in some domains, such methods depend on domain dependent heuristics that have to be exploited by their users. We propose a heuristic-independent mechanism by detecting backtracking. The method uses a dead-end path as a negative explanation tree, compares it with positive one, and finds a first different node to remove its corresponding rule by composing a macro. Repeated substructures in such a macro are then combined by applying the generalize-number technique and by sharing common substructures. Experimental results in STRIPS domain show that, by selecting an appropriate set of macros, 1) backtracking in solving training examples are suppressed, 2) its problem solving efficiency does not deteriorate even after learning a number of examples, 3) after learning 30 training examples, no backtracking occurs in solving 100 test examples different from the training examples. In conclusion, the proposed method speeds up the problem solving from 10 to 100 times.
Inverting Entailment and Progol
Authors: Muggleton Stephen 
Published: November 2023

Abstract
This paper firstly provides a re-appraisal of the development of techniques for inverting deduction, secondly introduces Mode-Directed Inverse Entailment (MDIE) as a generalisation and enhancement of previous approaches and thirdly describes an implementation of MDIE in the Progol system. Progol is implemented in C and available by anonymous ftp. The re-assessment of previous techniques in terms of inverse entailment leads to new results for learning from positive data and inverting implication between pairs of clauses.
Discovery of Protein Structural Constraints in a Deductive Database using Inductive Logic Programming
Authors: King Ross D , Clark Dominic A , Shirazi Jack , Sternberg Michael J E 
Published: November 2023

Abstract
This paper describes a framework for the use of machine learning as a tool to aid scientists in the discovery of patterns in data. The framework is tested by the application of the inductive logic programming (ILP) program GOLEM to the discovery of constraints in the packing of beta-sheets in alpha/beta proteins. These constraints (rules) play a part in the protein folding problem, an important unsolved problem in molecular biology. Constraints were learnt for four features of beta-sheet packing: the winding direction of two sequential sheets, whether two sequential sheets pack parallel or anti-parallel, whether two sheets pack adjacently, and whether a beta-sheet is at an edge. Investigation of the constraints found revealed interesting patterns, some of which were previously known, others that were novel. Novel features include the discovery that the relationship between pairs of sequential strands is in general one of decreasing size, and that more sequential pairs of strands wind in the direction out than the direction in. We conclude that machine learning has a role for scientists as a pattern discovery tool.
Controlling a Steel Mill with BOXES
Authors: McGarity Michael , Sammut Claude , Clements David 
Published: November 2023

Abstract
We describe an application of the BOXES learning algorithm of Michie and Chambers (1968) to a large-scale, real-world problem, namely, learning to control a steel mill. By applying BOXES to a model of a skinpass mill (a type of steel mill), we find that the BOXES algorithm can be made to produce a robust controller relatively quickly. Various aspects of the BOXES algorithm are adapted for the higher dimensionality and noise present in the skinpass mill. These changes are critically examined to find those which give a better controller.
A Reasoning System for Legal Analogy
Authors: Haraguchi Makoto 
Published: November 2023

Abstract
This paper presents a reasoning system that performs analogical reasoning for legal rules under an order-sorted representation. Analogy is often used in the domain of law to derive an appropriate conclusion for a particular case not covered by any legal rule. For such a case, lawyers are considered to find a legal rule whose requirement is similar to the case with respect to some significant points. Then the rule is analogically applied to the case for which the same or a similar conclusion of the rule is derived.
Kouichi Hirata
Authors: Hirata Kouichi 
Published: November 2023

Abstract
Abduction is a methodology of scientific inquiry. Peirce showed three types of abduction, and expressed them by one syllogism. In computer science, especially in the fields of automated reasoning and machine learning, various researches on abduction or abductive logic have been developed. In order to systematically understand such researches and to clearly discuss abduction, this paper classifies abduction into five types. This new classification is based on an interpretation of the syllogism in abduction and the definitions of hypotheses. We examine various researches on abduction so far developed and show that many researches on abduction can be placed in our classification. Furthermore, we discuss the most essential type of abduction in our classification for logic programming and default logic, and describe Prolog programs for abduction.
Toward incremental knowledge correction for agents in complex environments
Authors: Pearson Douglas J , Laird John E 
Published: November 2023

Abstract
In complex, dynamic environments, an agent’s domain knowledge will rarely be complete and correct. Existing deliberate approaches to domain theory correction are significantly restricted in the environments where they can be used. These systems are typically not used in agent-based tasks and rely on declarative representations to support nonincremental learning. This research investigates the use of procedural knowledge to support deliberate incremental error correction in complex environments. We describe a series of domain properties that constrain the error correction process and that are violated by existing approaches. We then present a procedural representation for domain knowledge which is sufficiently expressive, yet tractable. We develop a general framework for error detection and correction and then describe an error correction system, IMPROV, that uses our procedural representation to meet the constraints imposed by complex environments. Finally, we test the system in two sample domains and empirically demonstrate that it satisfies many of the constraints faced by agents in complex and challenging environments.
PAC-learning of preference relations over interpretations in lazy nonmonotonic reasoning
Authors: Satoh Ken 
Published: November 2023

Abstract
This paper presents a method of learning a class of preference order over interpretations in a propositional language which is a generalization of circumscription. The order considered in this paper is based on the distance in a weighted Euclidean space between the most preferable interpretation among all the interpretations and an interpretation represented as lattice points whose elements take values of either O or 1. Nonmonotonic reasoning defined by this order is a subclass of lazy nonmonotonic reasoning (Satoh 1992) where nonmonotonicity emerges only when new added knowledge is contradictory to the previous belief.
Drug design by machine learning
Authors: King Michael J E Sternberg* Ross D , Muggleton Ashwin Srinivasan Stephen H 
Published: November 2023

Abstract
Inductive logic programming, implemented in PROGOL, is used to predict whether members of a set of chemical compounds are mutagenic. This study is used to explore the general area of deriving structure-activity relationships, a central tool in rational drug design. The chemical structures are described in a general way by relational descriptors that encode atom types and their bond connectivities. This description contrasts with global attributes of the chemicals that are widely used in other studies such as regression. The results using PROGOL are compared to those from regression, neural nets and decision trees. Two data sets were considered - a regression friendly and a regression unfriendly set of compounds. PROGOL performed well on both data sets, in contrast to the other approaches that only performed well on one of these two sets of data.
A lecture and two radio broadcasts on machine intelligence by Alan Turing*
Authors: Copeland B Jack 
Published: November 2023

Abstract
Turing’s lecture ‘Intelligent Machinery, A Heretical Theory’ was given at Manchester University, probably in 1951. His ‘Can Digital Computers Think?’ was broadcast on the BBC Third Programme in May 1951. ‘Can Automatic Calculating Machines Be Said To Think?’, broadcast on the Third Programme in January 1952, is a discussion between Turing, Newman, Braithwaite and Jefferson. The appearance of these three items completes the publication of Turing’s surviving works on machine intelligence.
Heyday of the punched-card machine industry, the 1930s
Authors: Martin Campbell-Kelly 
Published: November 2023

Abstract
During the 1930s in Britain the traditional industries of shipbuilding, iron and steel, and coal fell into a decline from which they did not recover until the start of the Second World War. The social consequences of the 1930s economic depression, particularly in the North of England, have left an image of the 1930s in Britain that is in some ways misleading. For set against these declining industries there were, particularly in the South of England, bright new industries that recovered very rapidly from the 1929-1932 economic crisis, and during the second half of the 1930s grew at a prodigious rate. Typical of these expanding industries were chemicals, motor cars, and electrical machinery and appliances. 2 The punched card machine industry belonged to this prosperous sector of modern manufactured goods.
Pent-up demand, 1945-1949
Authors: Martin Campbell-Kelly 
Published: November 2023

Abstract
The post-war development of BTM and Powers, up to the merger in 1959, is encapsulated in the financial statistics of Tables 6.1 and 6.2. These tables show that both companies expanded almost without interruption throughout the fifteen-year period; even allowing for inflation, assets and production grew comfortably five-fold. The tables also show that while the companies were finely balanced in terms of size, in terms of profitability BTM was much the stronger.
The response to the IBM System/360, 1964-1965
Authors: Martin Campbell-Kelly 
Published: November 2023

Abstract
On 7 April 1964, IBM announced System/360, a compatible family of third-generation computers. System/360 consisted of six distinct processors and forty peripherals, which were intended to replace all IBM’s current computers, except the smallest and largest. The scale of the announcement was entirely unprecedented, and all the evidence is that it took the rest of the industry largely by surprise. Although ICT’s product planners had received intelligence in late 1963 that IBM was working on a compatible range of computers, they knew nothing of the range architecture, or when it would be launched.
Government relations and the formation of ICL, 1964-1968
Authors: Martin Campbell-Kelly 
Published: November 2023

Abstract
Servan-Schreiber’s famous book Le Défi Americain (The American Challenge) which appeared in 196 7, marked a turning point in the passive European acceptance of American domination of the market for hightechnolog goods. The book., vas a best-seller in France and in England, and it articulated the fears of politicians and industrialists, which had been growing for more than a decade. During the 1950s and early 1960s American manufacturers had increasingly come to dominate the advancedtechnology industries. These industries included aerospace, nuclear power and weapons, radar and defence electronics, and computers. AH of these were regarded as strategic industries, in the sense that it was considered that long-term prosperity, not to mention national prestige, depended upon them. The strategic importance of computers was highlighted in F ranee in 1965, when the United States Government placed an embargo on a CDC 6600 ordered by the French atomic research authorities. During the mid-1960s, European governments poured money into the high-technology industries, sometimes independently, sometimes as international consortia. A sprinkling of the names of cherished, but often ultimately abandoned, projects is enough to evoke the spirit of the times: TSR-2, Concorde, ELDO and Euroatom, and many more.
ICL and the New Range, 1968-1972
Authors: Martin Campbell-Kelly 
Published: November 2023

Abstract
The financial statistics of ICL (Table 13.1) have an uncomfortable similarity to those of ICT during the 1960s. Although revenues increased at an average annual rate of about 15 per cent, taking inflation into account this was no better than 10 per cent; this gentle growth was punctuated by two financial crises, in 1971-72 and 1980-81, with attendant reductions in profits and in the head count.