Kowalski, R., 1974. Logic for problem solving, University of Edinburgh, Department of Artificial Intelligence, DCL memo 75.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....access to data and strongly characterises the paradigm, contributing to its declarative flavour. Indeed, it enhances the ability to describe the results of a computation rather than the way such results are achieved, or, in other words to write what a programmer wants and not how to get it [76]. The multiple tuple spaces model is a far reaching extension of the paradigm: a system is composed of a set of tuple spaces, instead of a single one. Processes are connected to a tuple space, react to its contents, update it, and are allowed to modify the state of remote partners as well, by ....

....receive external inputs, test complex multiple conditions, and produce proper outputs. Logic programming is one of the most advanced and refined approaches for solving complex programming problems. It comes from the synergism between logic (declarativeness) and programming (procedurality) [76, 122]. The role of logic is to provide a formal framework to define the semantics of programs and to make them more understandable. The role of programming is to deal with the computational aspects of programs and, from the logicians point of view, to control how the proof (i.e. the program ....

R. Kowalski. Logic for Problem Solving. North-Holland, 1979.

....stay with the DEC 10 family of Prologs until you are more confident. The useful books are [Ennals, 1982] for a very simple introduction and [Clark McCabe, 1984] for a more ambitious and determined student. The best book on the market is probably [Conlon, 1985] For reading further afield then [Kowalski, 1979] is probably the classic. Also, Hogger, 1984] is a very worthwhile introduction to logic programming. The work of Lloyd provides those interested in theory with a very thorough analysis of the foundations of logic programming [Lloyd, 1987] Further ideas for reading can be gleaned from [Balbin ....

Kowalski, R. (1979). Logic for Problem Solving. Artificial Intelligence Series, North Holland.

....addressed with the alternative notion of successor state axioms just as well. The inferential advantage of the alternative design shows if we represent the Much like [13] roots in the axiomatization technique of [6] the foundations for the alternative form of successor state axioms were laid in [10]. collection of fluents that are true in a situation s by equating the atomic formula Holds(f, s) with the conditions on f to hold in s. The following formula, for instance, constitutes a suitable description of the initial situation in our example: 11) The crucial feature of this ....

R. Kowalski. Logic for Problem Solving. Elsevier, 1979.

....by assuming certain properties of the conditional probabilities. 2.2. Non statistical fusion techniques Besides the statistical approach, other techniques for decision level fusion exist. Common techniques include applications of Dempster Shafer theory [35] fuzzy logic [36] rule based fusion [37], and voting techniques [38] Although these techniques are not statistical, they can be described with discriminant functions in sensor confidence space with corresponding error functions. This notion allows us to compare different fusion methods on the capability of the method to produce a ....

R. Kowalski, Logic for Problem Solving, North-Holland, Amsterdam, 1979.

....one of the forms: j where j is an integer such that j 0 card(t) Further details can be found in [11] 4. Tools for Writing the Analyser A brief and informal introduction to logic programs and metamorphosis grammars is given here, for the sake of completeness. Fuller accounts can be found in [5,19,20,29]. 4.1 On Logic Programming Logic programs are essentially sets of clauses of the form: B A1,A2, An called Horn clauses, where B and Ai are atomic formulas, and all variables in the atomic formulas are under stood to be universally quantified. is read if , and the commas stand ....

Kowalski R. Logic for problem solving. North-Holland, 1979.

....4.1 Representation of Structure Charts A unique feature of logic programming is that one can use a set of relations to represent a data structure. We shall not discuss the pros and cons of term based representation and relationbased representation here (see, for example, Kowalski s text [9] ) Our contention is that a structure chart is usually large, and hence it would be rather cumbersome to encode the chart as one huge term [15] We need to know (a) all the modules in the chart, b) all the data items in the chart, c) the organization of the modules, and (d) all the ....

R.A. Kowalski, Logic for Problem Solving, North-Holland, Amsterdam (1979).

....which makes it possible to characterize aspects of nonmonotonic reasoning in an elegant way. The metainterpretation approach can be taken further by making a representation of the object program an argument of the interpreter as is the case for the demo predicated which was suggested by Kowalski [22] . This means that a metavariable can represent an unknown hole in the program and in principle, demo should be able generate the remaining parts of the program as to make the goal argument provable. However, it took more than a decade before logically satisfactory implementations of demo ....

Kowalski, R., Logic for problem solving. North-Holland, 1979.

....is modelled by definite formul which are atomic (and conjunctions of atoms) and procedures are modelled by implicational definite formul. The universal quantifiers are used to express the generality of procedures and existentials are used to express what in Prolog are called logical variables [25]. Here, for simplicity, we suppress all first order and quantificational concerns. A bunched hereditary Harrop sequent is a sequent P G, where P is a bunch of definite formul, i.e. a program, consisting of data and procedures. Such sequents are the basis of the bunched logic programming ....

R. Kowalski. Logic for Problem-solving. North-Holland, Elsevier, 1979.

....is modelled by definite formul which are atomic (and conjunctions of atoms) and procedures are modelled by implicational definite formul. The universal quantifiers are used to express the generality of procedures and existentials are used to express what in Prolog are called logical variables [25]. Here, for simplicity, we suppress all first order and quantificational concerns. A bunched hereditary Harrop sequent is a sequent P G, where P is a bunch of definite formul, i.e. a program, consisting of data and procedures. Such sequents are the basis of the bunched logic programming ....

R. Kowalski. Logic for Problem-solving. North-Holland, Elsevier, 1979.

....sharing and hiding, etc. which are all important means to manage the complexity of a particular domain. The desire to have in Prolog means for dividing a program into smaller relatively separated and independent units with transparent minimal interfaces has been responded by several authors [21, 11, 18, 16, 15, 19, 26]. Problems are encounteredwhen trying to combine logic databases (modules) Several approaches have been tried e.g. inheritance [19] context switching [16] introducing implication into goals [11] different definitions of visibility of atoms [18] using abstraction in separating the logic ....

R.A. Kowalski. Logic for problem solving. North-Holland, 1979.

....Related Work Historically, the field of AI has largely adopted an inference based problem solving perspective. Typical AI systems are programmed declaratively, and they rely on built in inference mechanisms for computing the desired quantities. A typical example is the Prolog programming language [54], where programs are collection of Horn clauses, and a built in logical inference mechanism (a theorem prover) is used to generate the program s output. Another popular example is Bayes networks [41, 81] where programmers specify probability distributions using a graphical language, and built in ....

R.A. Kowalski. Logic for Problem Solving. North Holland, 1979.

....in a solid world are features of LSD that will be exploited in the following sections. Since LSD is central to our presentation, we will briefly and informally introduce it using the example of designing keys. Because LSD is a logic programming language, it has some similarities with Prolog [10]. We will trade on those similarities to simplify our description of LSD. Formal and comprehensive discussions of Lograph and LSD can be found elsewhere, for example [5,7] A key solid consists of four types of components, a handle, a number of bits, a number of levellers, and a tip. Solids ....

R.A. Kowalski, Logic for problem solving, NorthHolland, (1979).

.... In the design of Prolog equality was avoided like the plaque though one reason for considering Horn clauses was the observation that functions can more naturally be expressed by Horn on leave from FG Intellektik, FB Informatik, TH Darmstadt,West Germany clauses than by non Horn clauses [Kowalski, 1979]. In mathematics as well as in computer science, however, functions are often specified by equations and the need to treat those theories in areas like automatic theorem proving and logic programming was felt soon. To examplify the advantage of using equational axioms consider a plan formation ....

....a sequence of actions, which when executed transform a current state into a goal state. The particular problem encountered in this task is the so called frame problem [McCarthy and Hayes, 1969] i.e. the problem to derive the assertions about a state which are not affected by an action. Kowalski [Kowalski, 1979] presents a solution via an axiom Holds(u; result(v; w) Holds(u; w) Preserves(v; u) which can be read declaratively as assertion u holds in the state which results from the application of action v in state w if u holds in w and v preserves u . The obvious problem with this axiom is to ....

Kowalski, R. (1979). Logic for Problem Solving, volume 7 of Artificial Intelligence. North Holland, New York/Oxford. 18

....of a complex proposition prob(r) and thus the conditional probability of two complex propositions prob(s r) The information is handled in the shape of clauses. Part Two. We will use the last structure to be applied in Artificial Intelligence problems whose knowledge base is made up of rules [3]. Firstly we describe the model of knowledge over which we will work afterwards. Secondly, we propose two examples in which some applications of this approach can be seen; being an approximate reasoning system one of this. This approximate reasoning system is able to provide actions in ....

....I Vc I = 2n ni, where n. number of literals of c. Therefore, we only need to calculate a Normal Disjunctive Form of r to calculate prob(r) IV I IvI : IV I 2 n. As it is well Known, every m CP may be written in a Canonical Disjunctive Form, but it is necessary a very inefficient solution [3]. We will introduce a more efficient normal disjunctive form. This form can be applied to clauses (rules) and conjunctions of clauses (Knowledge Base of rules) and it can be easely extended to another kind of complex propositions. 2.1. Valuations of a disjoin disjunction. Given r,s CP, it is ....

Kowalski, R. (1979). Logic for Problem Solving. North-Holland.

....form, named SAT, is well known to be NP complete [7] and plays a protagonist role in mathematical logic and computing theory. A SAT formulation can be used to solve the problem of logical implication, that is to detect if a given proposition is logically implied by a set of propositions [8] [9], 10] In the case of questionnaires, every edit can be encoded in a propo sitional logic clause. Moreover, since edits have a very precise syntax, encoding could be performed by means of the following automatic procedure. Edit propositional encoding procedure 1. Identification of the domains ....

....equal to 100. a) is clearly redundant. role head of the house) A (annual income 100) a) ual income 100 (b) A SAT formulation is used to solve the problem of logical implication. Given a set of statements and a single statement s, s if and only if J s is an unsatisfiable formula [9] [10] Therefore, the following holds. Theorem 3.2. The clausal representation of an edit ej is implied by the clausal representation of a set of edits E if and only if E J ej i unsatist]able. It can be consequently checked if an edit with clausal representation ej is re dundant by testing if ....

R. Kowalski. Logic for Problem solving. North Holland, 1978.

....of the development life cycle. One important characteristic of our system is the concept of representational independence: while the conceptual and representational constructs can combined in a flexible way in the modeling environment, they are implemented separately. The use of Prolog (e.g. [13, 3, 2, 23]) for completely building our CASE environment is essential to our approach. First, the declarative nature of Prolog offers, of course, several well known advantages over conventional programming languages such as clarity, modularity, conciseness, and legibility. As a result, programs are much ....

R. Kowalski. Logic for Problem Solving. North-Holland, 1979.

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Kowalski, R., 1974. Logic for problem solving, University of Edinburgh, Department of Artificial Intelligence, DCL memo 75.

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R. Kowalski, Logic for Problem Solving, North Holland (1979).

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R. Kowalski. Logic for Problem Solving. North Holland, 1979.

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R. A. Kowalski. Logic for Problem Solving. North-Holland, 1979.

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R. Kowalski. Logic for Problem Solving. North-Holland, Amsterdam, 1979.

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Kowalski, R., Logic for Problem Solving , North Holland, 1979.

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R. Kowalski. Logic for Problem Solving. North-Holland, 1979.

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Kowalski, R., Logic for Problem Solving, North Holland, New York (1979)

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Kowalski, Robert, 1980. Logic for Problem Solving, North Holland, New York.

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