Search

Multi‐adjoint relation equations are defined by means of a sup‐composition operator involving different conjunctions. Former works reveal that the resolution of a multi‐adjoint relation equation is closely related to such conjunctions. This paper presents a first approach on the study of the influence of the selection of a sup‐composition operator in different aspects of multi‐adjoint relation equations, as their solvability or the extremal elements of their solution set. The obtained results are illustrated by an example concerning multi‐adjoint logic programming. Last but not least, a procedure for reducing the computation of the solution set when only the sup‐composition operator is modified is also introduced. [ABSTRACT FROM AUTHOR]

Artificial temporal signaling systems, which mimic living out‐of‐equilibrium conditions, have made large progress. However, systems programmed by enzymatic reaction networks in multicomponent and unknown environments, and using biocompatible components remain a challenge. Herein, we demonstrate an approach to program temporal pH signals by enzymatic logic gates. They are realized by an enzymatic disaccharide‐to‐monosaccharide‐to‐sugar acid reaction cascade catalyzed by two metabolic chains: invertase‐glucose oxidase and β‐galactosidase‐glucose oxidase, respectively. Lifetimes of the transient pH signal can be programmed from less than 15 min to more than 1 day. We study enzymatic kinetics of the reaction cascades and reveal the underlying regulatory mechanisms. Operating with all‐food grade chemicals and coupling to self‐regulating hydrogel, our system is quite robust to work in a complicated medium with unknown components and in a biocompatible fashion. [ABSTRACT FROM AUTHOR]

Artificial temporal signaling systems, which mimic living out‐of‐equilibrium conditions, have made large progress. However, systems programmed by enzymatic reaction networks in multicomponent and unknown environments, and using biocompatible components remain a challenge. Herein, we demonstrate an approach to program temporal pH signals by enzymatic logic gates. They are realized by an enzymatic disaccharide‐to‐monosaccharide‐to‐sugar acid reaction cascade catalyzed by two metabolic chains: invertase‐glucose oxidase and β‐galactosidase‐glucose oxidase, respectively. Lifetimes of the transient pH signal can be programmed from less than 15 min to more than 1 day. We study enzymatic kinetics of the reaction cascades and reveal the underlying regulatory mechanisms. Operating with all‐food grade chemicals and coupling to self‐regulating hydrogel, our system is quite robust to work in a complicated medium with unknown components and in a biocompatible fashion. [ABSTRACT FROM AUTHOR]

Constructionism, long before it had a name, was intimately tied to the field of Artificial Intelligence. Soon after the birth of Logo at BBN, Seymour Papert set up the Logo Group as part of the MIT AI Lab. Logo was based upon Lisp, the first prominent AI programming language. Many early Logo activities involved natural language processing, robotics, artificial game players, and generating poetry, art, and music. In the 1970s researchers explored enhancements to Logo to support AI programming by children. In the 1980s the Prolog community, inspired by Logo's successes, began exploring how to adapt logic programming for use by school children. While there have been over 40 years of active AI research in creating intelligent tutoring systems, there was little AI‐flavoured constructionism after the 1980s until about 2017 when suddenly a great deal of activity started. Amongst those activities were attempts to enhance Scratch, Snap!, and MIT App Inventor with new blocks for speech synthesis, speech recognition, image recognition, and the use of pre‐trained deep learning models. The Snap! enhancements also include support for word embeddings, as well as blocks to enable learners to create, train, and use deep neural networks. Student and teacher project‐oriented resources highlighting these new AI programming components appeared at the same time. In this paper, we review this history, providing a unique perspective on AI developments—both social and technical—from a constructionist perspective. Reflecting on these, we close with speculations about possible futures for AI and constructionism. Practitioner notesWhat is already known about this topicThere exist excellent broad surveys of the current status of teaching machine learning in schools, for example Marques et al. (2020). There are historical collections of AI and education research papers that include descriptions of constructionist activities, for example Yazdani (1984).What this paper addsThis paper adds an in‐depth focus on historical and current efforts on AI and education that support constructionist teaching. This focus enables us to delve deeper than a broad survey. Uniquely, we provide a 50‐year historical perspective on constructionist AI tools, trials, and research. Grounded in this history and our survey of current tools and projects, we provide speculations about future directions.Implications for practice and/or policyWe hope our descriptions of current AI programming tools for non‐experts placed in a broad historical context will be of use to teachers wishing to introduce AI to their students in a constructionist manner, as well as to developers and researchers aiming to support such teaching. [ABSTRACT FROM AUTHOR]

Inductive logic programming (ILP) induces concepts from a set of positive examples, a set of negative examples, and background knowledge. ILP has been applied on tasks such as natural language processing, finite element mesh design, network mining, robotics, and drug discovery. These data sets usually contain numerical and multivalued categorical attributes; however, only a few relational learning systems are capable of handling them in an efficient way. In this paper, we present an evolutionary approach, called Grouping and Discretization for Enriching the Background Knowledge (GDEBaK), to deal with numerical and multivalued categorical attributes in ILP. This method uses evolutionary operators to create and test numerical splits and subsets of categorical values in accordance with a fitness function. The best subintervals and subsets are added to the background knowledge before constructing candidate hypotheses. We implemented GDEBaK embedded in Aleph and compared it to lazy discretization in Aleph and discretization in Top‐down Induction of Logical Decision Trees (TILDE) systems. The results obtained showed that our method improves accuracy and reduces the number of rules in most cases. Finally, we discuss these results and possible lines for future work. [ABSTRACT FROM AUTHOR]

The Semantic Web ontologies can contain vague axioms, which means the knowledge about them is imprecise and then query answering will not possible due to the open world assumption if the necessary information is incomplete (there is an ignorance about information). An axiom description can be very exact (crisp axiom) or exact (fuzzy axiom) if its knowledge is complete, otherwise it is inexact (vague axiom) if its knowledge is incomplete. Here, the author proposes vagueness description with meta‐level logic programming to describe vague ontologies. These vagueness descriptions are inputs to vagueness reasoning procedure implemented at meta‐level, which is based on extended tableau algorithm. The extended tableau algorithm is intended to answer queries even with the presence of imprecise information. [ABSTRACT FROM AUTHOR]

Here, the authors present a study on a contactless interface between a fully integrated wireless power receiver (WPR) and a dipole antenna. The authors analyse the impedance matching between the antenna and the inductive link formed by the contactless interface for providing an appropriate design methodology. Furthermore, the authors developed a measurement strategy to verify the entire system comprised by a pair of dipole antennas, the inductive link, and the CMOS WPR. The results show that the CMOS die is well coupled to the antenna, the complete system operates correctly with a source of 26.6 dBm at 987 MHz when the distance between the dipole antennas is 58 cm. [ABSTRACT FROM AUTHOR]

Abstract: Nonvolatile optoelectronic memories integrated with the functions of sensing, data storage, and data processing are promising for the potential Internet of things (IoT) applications. To meet the requirements of IoT devices, multifunctional memory devices with low power consumption and secure data storage are highly desirable. This study demonstrates an optoelectronic resistive switching memory integrated with sensing and logic operations by adopting organic–inorganic hybrid CH3NH3PbI3−xClx perovskites, which possess unusual defect physics and excellent light absorption. The CH3NH3PbI3−xClx cell exhibits low operation voltage of 0.1 V with the assistance of light illumination, long‐term retention property, and multiple resistance states. Its unique optoelectronic characteristics enable to perform logic operation for inputting one electrical pulse and one optical signal, and detect the coincidence of electrical and optical signal as well. This design provides possibilities for smart sensor in IoT application. [ABSTRACT FROM AUTHOR]

Abstract: Logic Programming (LP) follows the declarative programming paradigm, which novice students often find hard to grasp. The limited availability of visual teaching aids for LP can lead to low motivation for learning. In this paper, we present a platform for teaching and learning Prolog in Virtual Worlds, which enables the visual interpretation and verification of program results in a straightforward fashion and requires students to adopt a collaborative problem‐solving approach. The results of the pilot application and student‐centered evaluation of the platform are encouraging regarding group learning performance and user experience. The paper contributes to current practice of teaching and learning LP by proposing a metaphor and a system that can empower the educational process with toy world examples visualized in a shared 3D environment. [ABSTRACT FROM AUTHOR]

The existing approaches that map the given explicit preferences into standard assumption-based argumentation (ABA) frameworks reveal some difficulties such as generating a huge number of rules. To overcome them, we present an assumption-based argumentation framework equipped with preferences (p_ABA). It increases the expressive power of ABA by incorporating preferences between sentences into the framework. The semantics of p_ABA is given by [ABSTRACT FROM AUTHOR]

An effective means to analyze mRNA expression data is to take advantage of established knowledge from pathway databases, using methods such as pathway-enrichment analyses. However, pathway databases are not case-specific and expression data could be used to infer gene-regulation patterns in the context of specific pathways. In addition, canonical pathways may not always describe the signaling mechanisms properly, because interactions can frequently occur between genes in different pathways. Relatively few methods have been proposed to date for generating and analyzing such networks, preserving the causality between gene interactions and reasoning over the qualitative logic of regulatory effects. We present an algorithm (MCWalk) integrated with a logic programming approach, to discover subgraphs in large-scale signaling networks by random walks in a fully automated pipeline. As an exemplary application, we uncover the signal transduction mechanisms in a gene interaction network describing hepatocyte growth factor-stimulated cell migration and proliferation from gene-expression measured with microarray and RT-qPCR using in-house perturbation experiments in a keratinocyte-fibroblast co-culture. The resulting subgraphs illustrate possible associations of hepatocyte growth factor receptor c-Met nodes, differentially expressed genes and cellular states. Using perturbation experiments and Answer Set programming, we are able to select those which are more consistent with the experimental data. We discover key regulator nodes by measuring the frequency with which they are traversed when connecting signaling between receptors and significantly regulated genes and predict their expression-shift consistently with the measured data. The Java implementation of MCWalk is publicly available under the MIT license at: . [ABSTRACT FROM AUTHOR]

Interpretation of spatial features often requires combined reasoning over geometry and semantics. We introduce the Space package, an open source SWI-Prolog extension that provides spatial indexing capabilities. Together with the existing semantic web reasoning capabilities of SWI-Prolog, this allows efficient integration of spatial and semantic queries and provides an infrastructure for declarative programming with space and semantics. There are few systems that provide indexing and reasoning facilities for both spatial and semantic data. A common solution is to combine separate semantic reasoning and geospatial services. Such loose coupling has the disadvantage that each service cannot make use of the statistics of the other. This makes optimization of such a service-oriented architecture hard. The SWI-Prolog Space and Semantic web packages provide a native Prolog interface to both spatial and semantic indexing and reasoning, which makes it easy to write combined query optimizers. Another advantage of the Space package is that it allows declarative logic programming, which means in practice that you say what you want to compute instead of how to compute it. The actual indexing machinery is encapsulated inside Prolog predicates. In this article we describe the interface of the Space package, compare its functionality to alternative software libraries, and show how to work with it using three example applications. These example illustrations include reasoning over movement patterns, dynamically loading geospatial linked data off the semantic web, and setting up a simple KML server. [ABSTRACT FROM AUTHOR]

Current expert system technology is 30 years old. Expert system shells find their origins in the work of early expert systems, most notably MYCIN which was developed at Stanford in the mid-1970s. Even Prolog programmers are settling for less robust reasoning power. The logic programming community (from which both expert systems and Prolog arose) has made notable advances since those times. These advances are lacking from current expert system technology. The advances include a well-developed theory of multiple forms of negation, an understanding of open domains and the closed world assumption, default reasoning with exceptions, reasoning with respect to time (i.e. a solution to the frame problem, and introspection with regard to previous beliefs), reasoning about actions, introspection, and maintaining multiple views of the world simultaneously. The contribution of this paper is to discuss these developments in a singular, integrated, practical, digestible manner. Some of these ideas exist in a variety of papers spread across decades. They also exist in the minds of a very small community of researchers. Some of these ideas are unpublished. The presentation in this paper is from a different point of view, and intended to be more comprehensive and pedagogical. The presentation is also intended to be accessible to a much wider audience. Both the synthesis and the simplicity of this presentation are absent from the literature. [ABSTRACT FROM AUTHOR]

The authors propose a system to evaluate multiple output logic functions using hardware based on a decision diagram. In addition, the authors propose a PMDD (Paged reduced ordered Multivalued Decision Diagram) as a data structure. Methods to evaluate a logic function in hardware include methods based on an SBDD (Shared reduced ordered Binary Decision Diagram). The method based on a PMDD evaluates multiple output functions using several computational units, and as a result is faster compared to methods based on an SBDD. The multiple output logic function to be created is represented using a PMDD and stored in memory. Then a control circuit which can search the PMDD is added. The authors compare the memory capacity and processing time required for various benchmark functions for the SBDD and their PMDD. © 2004 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 87(9): 73–81, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.20097 [ABSTRACT FROM AUTHOR]

Preference elicitation is a serious bottleneck in many decision support applications and agent specification tasks. Ceteris paribus (CP)-nets were designed to make the process of preference elicitation simpler and more intuitive for lay users by graphically structuring a set of CP preference statements—preference statements that most people find natural and intuitive. Beside their usefulness in the process of preference elicitation, CP-nets support efficient optimization algorithms that are crucial in most applications (e.g., the selection of the best action to execute or the best product configuration). In various contexts, CP-nets with an underlying cyclic structure emerge naturally. Often, they are inconsistent according to the current semantics, and the user is required to revise them. In this paper, we show how optimization queries can be meaningfully answered in many “inconsistent” networks without troubling the user with requests for revisions. In addition, we describe a method for focusing the user's revision process when revisions are truly needed. In the process, we provide a formal semantics that justifies our approach and new techniques for computing optimal outcomes. Some of the methods we use are based on a reduction to the problem of computing stable models for nonmonotonic logic programs, and we explore this relationship closely. [ABSTRACT FROM AUTHOR]

Logic programs with ordered disjunction (LPODs) contain a new connective which allows representing alternative, ranked options for problem solutions in the heads of rules: A× B intuitively means that if possible A, but if A is not possible, then at least B. The semantics of logic programs with ordered disjunction is based on a preference relation on answer sets. We show how LPODs can be implemented using answer set solvers for normal programs. The implementation is based on a generator, which produces candidate answer sets and a tester which checks whether a given candidate is maximally preferred and produces a better candidate if it is not. We also discuss the complexity of reasoning tasks based on LPODs and possible applications. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of modeling Web information resources using expert knowledge and personalized user information for improved Web searching capabilities. We propose a "Web information space" model, which is composed of Web-based information resources (HTML/XML [Hypertext Markup Language/Extensible Markup Language] documents on the Web), expert advice repositories (domain-expert-specified metadata for information resources), and personalized information about users (captured as user profiles that indicate users' preferences about experts as well as users' knowledge about topics). Expert advice, the heart of the Web information space model, is specified using topics and relationships among topics (called metalinks), along the lines of the recently proposed topic maps. Topics and metalinks constitute metadata that describe the contents of the underlying HTML/XML Web resources. The metadata specification process is semiautomated, and it exploits XML DTDs (Document Type Definition) to allow domain-expert guided mapping of DTD elements to topics and metalinks. The expert advice is stored in an object-relational database management system (DBMS). To demonstrate the practicality and usability of the proposed Web information space model, we created a prototype expert advice repository of more than one million topics/metalinks for DBLP (Database and Logic Programming) Bibliography data set. We also present a query interface that provides sophisticated querying facilities for DBLP Bibliography resources using the expert advice repository. [ABSTRACT FROM AUTHOR]

The auction in contract bridge is a game with incomplete information. This paper models the process as an interaction among agents with a hypothetical reasoning mechanism. The criterion for the action of each agent is defined as “maximizing gain by cooperating with the partner and minimizing loss by competing with the opponents.” An agent with the following characteristics is created. Based on the course of bidding, the hands of both sides are estimated by hypothetical reasoning. Then flexible bids are made in an effort to obtain the contract with the greatest gain or smallest loss of points. Experiments show that in a game with incomplete information, it is useful not only to send information to the partner, but also to disrupt information exchange by the opponents. The effect of ambiguity of information on the behavior of both sides is discussed. © 2003 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 86(12): 76–86, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.10144 [ABSTRACT FROM AUTHOR]

We have developed a production scheduling system called “K-CHIP,” which handles operator resource leveling, using CLP (constraint logic programming). This system has the following features: (1) it approaches constraint satisfaction problems (CSP) as two-phase problems, consisting of production line scheduling and operator assignment scheduling, (2) it uses an interactive method for relaxing constraints on operator skills and on the number of operators as the intra-CSP adjustment method, and (3) it guarantees skill constraint satisfaction by creating a future training schedule. A simulation carried out based on this system produced the prospect of a 33% reduction in manpower. The simulation also demonstrated that the method can be effectively utilized to expand the system integration functions, centered on skills that are the main concern in plant management. © 2003 Wiley Periodicals, Inc. Electron Comm Jpn Pt 2, 86(6): 62–72, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjb.10156 [ABSTRACT FROM AUTHOR]

We propose a semantics for disjunctive logic programs, based on the single notion of forcing. We show that the semantics properly extends, in a natural way, previous approaches. A fixpoint characterization is also provided. We also take a closer look at the relationship between disjunctive logic programs and disjunctive-free logic programs. We present certain criteria under which a disjunctive program is semantically equivalent with its disjunctive-free (shifted) version. [ABSTRACT FROM AUTHOR]

This paper applies rough set theory to Inductive Logic Programming (ILP, a relatively new method in machine learning) to deal with imperfect data occurring in large real-world applications. We investigate various kinds of imperfect data in ILP and propose rough problem settings to deal with incomplete background knowledge (where essential predicates/clauses are missing), indiscernible data (where some examples belong to both sets of positive and negative training examples), missing classification (where some examples are unclassified) and too strong declarative bias (hence the failure in searching for solutions). The rough problem settings relax the strict requirements in the standard normal problem setting for ILP, so that rough but useful hypotheses can be induced from imperfect data. We give simple measures of learning quality for the rough problem settings. For other kinds of imperfect data (noise data, too sparse data, missing values, real-valued data, etc.), while referring to their traditional handling techniques, we also point out the possibility of new methods based on rough set theory. [ABSTRACT FROM AUTHOR]

The Variable Precision Rough Set Inductive Logic Programming model (VPRSILP model) extends the Variable Precision Rough Set (VPRS) model to Inductive Logic Programming (ILP). The generic Rough Set Inductive Logic Programming (gRS-ILP) model provides a framework for ILP when the setting is imprecise and any induced logic program will not be able to distinguish between certain positive and negative examples. The gRS-ILP model is extended in this paper to the VPRSILP model by including features of the VPRS model. The VPRSILP model is applied to strings and an illustrative experiment on transmembrane domains in amino acid sequences is presented. [ABSTRACT FROM AUTHOR]

Presents a framework that combines Genetic Programming and Inductive Logic Programming to induce knowledge represented in knowledge representation formalisms from noisy databases. Problem in the systems' use of a limiting attibute-value language for representing training examples; Implementation of the framework LOGENPRO.

The land mobile communication is affected by fading when the mobile station moves in the multipath transmission environment. On the other hand, there are cases where there is no fluting as in the communication with the standing or transported mobile station. It has been understood that the coherent detection using PLL is better in the nonfading environment sad the differential detection is better in the fading environment. From such a viewpoint, this paper proposes s detector diversity system for mobile communication which can cope with the complicated states of the transmission path by switching between the coherent detection (PLL) sad the differential detection using the error-detection code. The proposed system uses a simple error-detection code, and the two detectors are switched by selecting the detector with a fewer number of detected errors. The feature of the system is that it is simple, being constructed by the existing PLL and the differential detector. The effectiveness of the proposed system is verified by computer simulation. [ABSTRACT FROM AUTHOR]

The intelligent network (IN) is expected to play an important role in rapidly and efficiently introducing new telecommunication services. This paper discusses the abstraction and control methods for constructing a service control environment in the intelligent network which does not depend on specific service. By using this method, a real he, work configuration is abstracted into several patterns, considering the control points required by each service specification and each pattern can be described as a combination of functional virtualized resources. It is made possible to handle the diversified service specifications in a unified way which is independent of detailed conditions of the transport network. It is made possible also to ensure the service independency of the control interface specifying the interface by control of each virtualized resources. Moreover, a model for the call control is proposed and the function assignment in introducing IN into existing network is discussed. [ABSTRACT FROM AUTHOR]

In signal processing applications, it sometimes happens that a problem that is difficult to process based on the linear theory can be solved by nonlinear processing. When the unknown system is nonlinear, however, it is difficult to process the estimation problem in real time by the conventional nonlinear processing method, since a large amount of computation is required in order to determine the optimal solution. On the other hand, neural networks with a non-linear input-output relation are considered as interesting and are applied to various problems such as pattern recognition and the estimation of nonlinear systems. This paper considers a nonlinear adaptive digital filter that has a large number of parameters, with a requirement for real-time processing. A method of designing a parallel recurrent neural digital filter by introducing multiple, small-scale recurrent neural networks is proposed. Comparing the proposed method to the conventional method based on the linear or nonlinear theories, a better result is obtained by the proposed method. As another aspect, learning efficiency can be improved by the proposed method since parallel learning is executed. © 1997 Scripta Technica, Inc. Electron Comm Jpn Pt 3, 80(3): 83–93, 1997 [ABSTRACT FROM AUTHOR]

The article focuses on the heirarchy proposed by A. Grzegorcczyk in the 1950s. It mentions that each of the heirarchy is built from a family of functions. It adds that the relation associated with a function that can be computed is defined as sets of zeros of the function. Moreover, it presents an inductive characterization of the heirarchy where n>3.