Information & Computation
About: Information & Computation is an academic journal published by Academic Press. The journal publishes majorly in the area(s): Decidability & Time complexity. It has an ISSN identifier of 0890-5401. Over the lifetime, 5098 publications have been published receiving 197648 citations. The journal is also known as: information artifact & info.
Papers published on a yearly basis
TL;DR: A proposed schema and some detailed specifications for constructing a learning system by means of programming a computer are given, trying to separate learning processes and problem-solving techniques from specific problem content in order to achieve generality.
Abstract: This paper reports on a proposed schema and gives some detailed specifications for constructing a learning system by means of programming a computer. We have tried to separate learning processes and problem-solving techniques from specific problem content in order to achieve generality, i.e., in order to achieve a system capable of performing in a wide variety of learning and problem-solving situations. Behavior of the system is determined by both a direct and an indirect means. The former involves detailed, explicit specification of responses or response patterns in the form of built-in programs. The indirect means is by programs representing three mechanisms: a “community unit” (a program-providing mechanism), a planning mechanism, and an induction mechanism. These mechanisms have in common the following features: (1) a directly given repertory of response patterns; (2) general and less explicitly specified decision making rules and hierarchically distributed authority for decision making; (3) an ability to delegate some control over the system's behavior to the environment; and (4) a self-modifying ability which allows the decision-making rules and the repertory of response patterns to adapt and grow. In Part I of this paper, the community unit is described and an illustration of its operation is given. It is presented in a schematized framework as a team of routines connected by first and second-order feedback loops. The function of the community unit is to provide higher-level programs (its environment or customers) with programs capable of performing requested tasks, or to perform a customer-stipulated task by executing a program. If the community unit does not have a ready-made program in stock to fill a particular request, internal programming will be performed, i.e., the community unit will have to construct a program, and debug it, before outputting or executing it. The primary purpose of internal programming is to assist higher-level programs in performing tasks for which detailed preplanning by an external programmer is either impossible or impractical. Some heuristics are suggested for enabling the community unit to search for a usable sequence of operations more efficiently than if it were to search simply by exhaustive or random trial and error. These heuristics are of a step-by-step nature. For complex problems, however, such step-by-step heuristics alone will fail unless there is also a mechanism for analyzing problem structure and placing guideposts on the road to the goal. A planning mechanism capable of doing this is proposed in Part II. Under the control of a higher-level program which specifies the level of detail required in a plan being developed, this planning mechanism is to break up problems into a hierarchy of subproblems each by itself presumably easier to solve than the original problem. To manage classes of problems and to make efficient use of past experience, an induction mechanism is proposed in Part II. An illustration is given of the induction mechanism solving a specific sequence of tasks. The system is currently being programmed and tested in IPL-V on the Philco 2000 computer. The current stage of the programming effort is reported in an epilogue to Part II.
TL;DR: It was found that theclass of context-sensitive languages is learnable from an informant, but that not even the class of regular languages is learningable from a text.
Abstract: Language learnability has been investigated. This refers to the following situation: A class of possible languages is specified, together with a method of presenting information to the learner about an unknown language, which is to be chosen from the class. The question is now asked, “Is the information sufficient to determine which of the possible languages is the unknown language?” Many definitions of learnability are possible, but only the following is considered here: Time is quantized and has a finite starting time. At each time the learner receives a unit of information and is to make a guess as to the identity of the unknown language on the basis of the information received so far. This process continues forever. The class of languages will be considered learnable with respect to the specified method of information presentation if there is an algorithm that the learner can use to make his guesses, the algorithm having the following property: Given any language of the class, there is some finite time after which the guesses will all be the same and they will be correct. In this preliminary investigation, a language is taken to be a set of strings on some finite alphabet. The alphabet is the same for all languages of the class. Several variations of each of the following two basic methods of information presentation are investigated: A text for a language generates the strings of the language in any order such that every string of the language occurs at least once. An informant for a language tells whether a string is in the language, and chooses the strings in some order such that every string occurs at least once. It was found that the class of context-sensitive languages is learnable from an informant, but that not even the class of regular languages is learnable from a text.
TL;DR: The a-calculus is presented, a calculus of communicating systems in which one can naturally express processes which have changing structure, including the algebraic theory of strong bisimilarity and strong equivalence, including a new notion of equivalence indexed by distinctions.
Abstract: We present the a-calculus, a calculus of communicating systems in which one can naturally express processes which have changing structure. Not only may the component agents of a system be arbitrarily linked, but a communication between neighbours may carry information which changes that linkage. The calculus is an extension of the process algebra CCS, following work by Engberg and Nielsen, who added mobility to CCS while preserving its algebraic properties. The rr-calculus gains simplicity by removing all distinction between variables and constants; communication links are identified by names, and computation is represented purely as the communication of names across links. After an illustrated description of how the n-calculus generalises conventional process algebras in treating mobility, several examples exploiting mobility are given in some detail. The important examples are the encoding into the n-calculus of higher-order functions (the I-calculus and combinatory algebra), the transmission of processes as values, and the representation of data structures as processes. The paper continues by presenting the algebraic theory of strong bisimilarity and strong equivalence, including a new notion of equivalence indexed by distinctions-i.e., assumptions of inequality among names. These theories are based upon a semantics in terms of a labeled transition system and a notion of strong bisimulation, both of which are expounded in detail in a companion paper. We also report briefly on work-in-progress based upon the corresponding notion of weak bisimulation, in which internal actions cannot be observed. 0 1992 Academic Press, Inc.
TL;DR: In this article, the problem of identifying an unknown regular set from examples of its members and nonmembers is addressed, where the regular set is presented by a minimaMy adequate teacher, which can answer membership queries about the set and can also test a conjecture and indicate whether it is equal to the unknown set and provide a counterexample if not.
Abstract: The problem of identifying an unknown regular set from examples of its members and nonmembers is addressed. It is assumed that the regular set is presented by a minimaMy adequate Teacher, which can answer membership queries about the set and can also test a conjecture and indicate whether it is equal to the unknown set and provide a counterexample if not. (A counterexample is a string in the symmetric difference of the correct set and the conjectured set.) A learning algorithm L* is described that correctly learns any regular set from any minimally adequate Teacher in time polynomial in the number of states of the minimum dfa for the set and the maximum length of any counterexample provided by the Teacher. It is shown that in a stochastic setting the ability of the Teacher to test conjectures may be replaced by a random sampling oracle, EX( ). A polynomial-time learning algorithm is shown for a particular problem of context-free language identification.
TL;DR: A simple and effective method, based on weighted voting, is introduced for constructing a compound algorithm, which is robust in the presence of errors in the data, and is called the Weighted Majority Algorithm.
Abstract: We study the construction of prediction algorithms in a situation in which a learner faces a sequence of trials, with a prediction to be made in each, and the goal of the learner is to make few mistakes. We are interested in the case where the learner has reason to believe that one of some pool of known algorithms will perform well, but the learner does not know which one. A simple and effective method, based on weighted voting, is introduced for constructing a compound algorithm in such a circumstance. We call this method the Weighted Majority Algorithm. We show that this algorithm is robust in the presence of errors in the data. We discuss various versions of the Weighted Majority Algorithm and prove mistake bounds for them that are closely related to the mistake bounds of the best algorithms of the pool. For example, given a sequence of trials, if there is an algorithm in the pool A that makes at most m mistakes then the Weighted Majority Algorithm will make at most c(log |A| + m) mistakes on that sequence, where c is fixed constant.