Knowledge Representation In Artificial Intelligence Research Articles

Reducible Theories and Amalgamations of Models

Within knowledge representation in artificial intelligence, a first-order ontology is a theory in first-order logic that axiomatizes the concepts in some domain. Ontology verification is concerned with the relationship between the intended models of an ontology and the models of the axiomatization of the ontology. In particular, we want to characterize the models of an ontology up to isomorphism and determine whether or not these models are equivalent to the intended models of the ontology. Unfortunately, it can be quite difficult to characterize the models of an ontology up to isomorphism. In the first half of this article, we review the different metalogical relationships between first-order theories and identify which relationship is needed for ontology verification. In particular, we will demonstrate that the notion of logical synonymy is needed to specify a representation theorem for the class of models of one first-order ontology with respect to another. In the second half of the article, we discuss the notion of reducible theories and show we can specify representation theorems by which models are constructed by amalgamating models of the constituent ontologies.

Introduction to the Special Issue “Artificial Intelligence Knowledge Representation”

A guest editorial could have been written when this Special Issue was first announced, to stimulate submissions and guide prospect authors, yet writing it closer to the deadline (together with the deadline extension announcement) makes it possible to highlight recent developments which have happened since [...]

INFERENCE MENGGUNAKAN FORWARD CHAINING PADA FOOD AFFORDABILITY

Price, consumption, and production influence global food estimation. Food consumption and food production needs improving affordability, government policy and expert proposition in decision making. Expert have developed a scenario combining direct weather impact on key grain producing regions, indirect impact through crop pathogens, consequences for markets, and stock of global food. Global food production tended to increase, meanwhile consumption under production. In Indonesia, production is under consumption. It is anomaly by global situation and world condition. Government policy is tariff, export, and import. Export food influenced by price food commodity is not stable, meanwhile food import tended to increase because by national food consumption increasing demand. Government policy and expert proposition to modern society, need Expert System is increasing rapidly. In Artificial Intelligence, Expert System is a computer system that emulates the decision making ability of a human expert. There are two reasoning strategies in Expert System: Forward Chaining and Backward Chaining. The aim of this paper is to identify which reasoning strategy system (Forward Chaining). This paper focus on the concept of knowledge representation in Artificial Intelligence and implementing of Forward Chaining on Food Affordability. Food Affordability used to planning of food in the future. Finally, Inference using Forward Chaining on Food Affordability, the result it is good and can be used to planning and policy on national food.

Script knowledge representation in game designing for instructional media

Script is one of knowledge representation in artificial intelligence. Script consists of four parts: input conditions, tracks, properties, roles, scenes, and results. Script can be used to model artificial intelligence knowledge like in game designing. The use of script in game designing is relatively rare despite the fact that script representation provides comprehensive components to display a game. This research is aimed to use Script technique in game designing. To this end, a Multimedia Design Method was used. This method consisted of several steps. The first step was concept. The second was designing script, storyboard, and navigation structure. The third was material collecting. The fourth was assembly. The fifth was testing. At the testing stage, the application designer and users carried out alpha and beta testings. The sixth was distribution. At this last step, the application was stored into a media storage. The designing resulted in game software for instructional media for elementary school students. This research came into a conclusion that Script knowledge representation could model sufficient game design components.

Comparative Study Of Forward And Backward Chaining In Artificial Intelligence

An artificial intelligence system is capable of elucidating and representing knowledge along with storing and manipulating data. Knowledge could be a collection of facts and principles build up by human. It is the refined form of information. Knowledge representation is to represent knowledge in a manner that facilitates the power to draw conclusions from knowledge. Knowledge representation is a good approach as conventional procedural code is not the best way to use for solving complex problems. Frames, Semantic Nets, Systems Architecture, Rules, and Ontology are its techniques to represent knowledge. Forward and backward chaining are the two main methods of reasoning used in an inference engine. It is a very common approach for expert systems, business and systems. This paper focus on the concept of knowledge representation in artificial intelligence and the elaborating the comparison of forward and backward chaining.

Hybrid Systems for Knowledge Representation in Artificial Intelligence

There are few knowledge representation (KR) techniques available for efficiently representing knowledge. However, with the increase in complexity, better methods are needed. Some researchers came up with hybrid mechanisms by combining two or more methods. In an effort to construct an intelligent computer system, a primary consideration is to represent large amounts of knowledge in a way that allows effective use and efficiently organizing information to facilitate making the recommended inferences. There are merits and demerits of combinations, and standardized method of KR is needed. In this paper, various hybrid schemes of KR were explored at length and details presented.

Semantic Web: A road to the knowledge infrastructure on the internet

In this article, I describe the basic technologies for Semantic Web and relationship between Semantic Web and Knowledge Representation in Artificial Intelligence. Semantic Web is planned as an extension of the current web in order to help cooperation between computers and humans, i.e., computers and humans are expected to understand each other in the knowledge level. I first describe the vision of the Semantic Web, then introduce the current Semantic Web technologies, i.e., RDF, RDPS, and OWL. I describe relationship between the trend of Semantic Web and Knowledge Representation, and clarify challenges and difficulties of Semantic Web from the point of view of Knowledge Representation.

A note on current approaches to extending fuzzy logic to object-oriented modeling

In this study, we have attempted a survey of current approaches carried out in the confluence of the two technologies, fuzzy set theory and object-oriented technology, that could provide a powerful tool for enhancing database management systems, software modeling, and knowledge representation in artificial intelligence (AI) systems. Possible types of fuzziness are discussed and key features related to different kinds of fuzzy software systems are also pinpointed. In a nutshell, fuzzy theory, as a modeling mechanism, is especially useful in tackling real world applications whose complexity demands are growing intensively. © 2001 John Wiley & Sons, Inc.

An Information-Based Theory of Conditionals

We present an approach to combining three areas of research which we claim are all based on information theory: knowledge representation in Artificial Intelligence and Cognitive Science using prototypes, plans, or schemata; formal semantics in natural language, especially the semantics of the `if-then' conditional construct; and the logic of subjunctive conditionals first developed using a possible worlds semantics by Stalnaker and Lewis. The basic premise of the paper is that both schema-based inference and the semantics of conditionals are based on Dretske's notion of information flow and Barwise and Perry's notion of a constraint in situation semantics. That is, the connection between antecedent $A$ and consequent $B$ of a conditional `if $A$ were the case then $B$ would be the case' is an informational relation holding with respect to a pragmatically determined utterance situation. The bridge between AI and conditional logic is that a prototype or planning schema represents a situation type, and the background assumptions underlying the application of a schema in a situation correspond to channel conditions on the flow of information. Adapting the work of Stalnaker and Lewis, the semantics of conditionals is modeled by a refinement ordering on situations: a conditional `if $A$ then $B$' holds with respect to a situation if all the minimal refinements of the situation that support $A$ also support $B$. We present new logics of situations, information flow, and subjunctive conditionals based on three-valued partial logic that formalizes our approach, and conclude with a discussion of the resulting theory of conditionals, including the paradoxes of conditional implication, the difference between truth conditions and assertability conditions for subjunctive conditionals, and the relationship between subjunctive and indicative conditionals.

Embedding prioritized circumscription in disjunctive logic programs

In this paper, we present a method for embedding prioritized circumscription of a clausal theory into general disjunctive logic programs (GDP) with negation as failure in the head. In recent works, Sakama and Inoue show that parallel circumscription can be embedded in GDP. They also show that prioritized circumscription of a clausal theory can be represented in the framework of GDP extended with priorities. Wang et al. recently have also shown that prioritized circumscription can be expressed in their priority logic by applying monotonic inference rules constrained by a priority relation among the rules. In our method, the priorities of minimization in the circumscription policy are translated into the syntactical form of logic program rules, and the models of the circumscription are precisely captured by the stable models of the program. Thus we show that prioritized circumscription can be directly embedded in GDP without extending the GDP logic programming framework. This result further asserts the expressive power of the class of general disjunctive programs and supports its use for knowledge representation in Artificial Intelligence. The results of this paper also lead to embeddings of prioritized circumscription into the MBNF logic of Lifschitz and AE logic of Moore.

Hybrid architecture for shape reconstruction and object recognition

The proposed architecture is aimed to recover 3-D- shape information from gray-level images of a scene; to build a geometric representation of the scene in terms of geometric primitives; and to reason about the scene. The novelty of the architecture is in fact the integration of different approaches: symbolic reasoning techniques typical of knowledge representation in artificial intelligence, algorithmic capabilities typical of artificial vision schemes, and analogue techniques typical of artificial neural networks. Experimental results obtained by means of an implemented version of the proposed architecture acting on real scene images are reported to illustrate the system capabilities.

The least fixpoint transformation for disjunctive logic programs

The paradigm of disjunctive logic programming (DLP) enhances greatly the expressive power of normal logic programming (NLP) and many (declarative) semantics have been defined for DLP to cope with various problems of knowledge representation in artificial intelligence. However, the expressive ability of the semantics and the soundness of program transformations for DLP have been rarely explored. This paper defines an immediate consequence operatorT P G for each disjunctive program and shows thatT P G has the least and computable fixpointLft(P). Lft is, in fact, a program transformation for DLP, which transforms all disjunctive programs into negative programs. It is shown thatLft preserves many key semantics, including the disjunctive stable models, well-founded model, disjunctive argument semantics DAS, three-valued models, etc. This means that every disjunctive programP has a unique canonical formLft(P) with respect to these semantics. As a result, the work in this paper provides a unifying framework for studying the expressive ability of various semantics for DLP. On the other hand, the computing of the above semantics for negative programs is just a trivial task, therefore,Lft(P) is also an optimization method for DLP. Another application ofLft is to derive some interesting semantic results for DLP.

Principled modeling and automatic classification for enhancing the reusability of problem-solving methods of expert systems

Software reuse is widely believed to be a key to improving software productivity and quality in conventional software. In expert systems, much of the knowledge has been compiled (i.e., compressed and restricted into effective procedures) and this makes reusability difficult. One of the issues in modeling expert systems for enhanced reusability is capturing explicity the underlying problem solving designs. Principled knowledge representation schemes have been used to model components of complex software systems. However, the potential for applying these principled modeling techniques for explicitly capturing the problem solving designs of expert systems has not been fully explored. To overcome this omission, we use an Artificial Intelligence knowledge representation scheme for developing an ontology of the software components to facilitate their classification and retrieval. The application of our ontological approach is of both theoretical and practical significance. This method facilitates the reuse of high-level design. We illustrate the application of principled domain modeling using two real world applications of knowledge-based systems.

Knowledge representation in conceptual realism

Knowledge representation in Artificial Intelligence (AI) involves more than the representation of a large number of facts or beliefs regarding a given domain, i.e. more than a mere listing of those facts or beliefs as data structures. It may involve, for example, an account of the way the properties and relations that are known or believed to hold of the objects in that domain are organized into a theoretical whole—such as the way different branches of mathematics, or of physics and chemistry, or of biology and psychology, etc., are organized, and even the way different parts of our commonsense knowledge or beliefs about the world can be organized. But different theoretical accounts will apply to different domains, and one of the questions that arises here is whether or not there are categorial principles of representation and organization that apply across all domains regardless of the specific nature of the objects in those domains. If there are such principles, then they can serve as a basis for a general framework of knowledge representation independently of its application to particular domains. In what follows I will give a brief outline of some of the categorial structures of conceptual realism as a formal ontology. It is this system that I propose we adopt as the basis of a categorial framework for knowledge representation.

Logic programming and knowledge representation

In this paper, we review recent work aimed at the application of declarative logic programming to knowledge representation in artificial intelligence. We consider extensions of the language of definite logic programs by classical (strong) negation, disjunction, and some modal operators and show how each of the added features extends the representational power of the language. We also discuss extensions of logic programming allowing abductive reasoning, meta-reasoning and reasoning in open domains. We investigate the methodology of using these languages for representing various forms of nonmonotonic reasoning and for describing knowledge in specific domains. We also address recent work on properties of programs needed for successful applications of this methodology such as consistency, categoricity and complexity.

Qualitative reasoning about physical systems: an artificial intelligence perspective

Some interdisciplinary issues concerning artificial intelligence (AI) are explored in relation to modelling in physics and engineering. A short survey is given of automated qualitative reasoning about physical systems, which in recent years has become an active research area in AI, and has been partly influenced by system dynamics. The conceptual aspects of the AI approach to physical science are critically assessed, and the mutual relevance of the two disciplines is pointed out. In particular, we indicate how bond graphs can be interpreted as an AI knowledge representation language. On this basis, a knowledge-based approach, called qualitative bond analysis (QuBA), is discussed that exploits bond graphs such that a computational AI system is able to deduce qualitative information about physical systems.

The computational complexity of probabilistic inference using bayesian belief networks

Bayesian belief networks provide a natural, efficient method for representing probabilistic dependencies among a set of variables. For these reasons, numerous researchers are exploring the use of belief networks as a knowledge representation in artificial intelligence. Algorithms have been developed previously for efficient probabilistic inference using special classes of belief networks. More general classes of belief networks, however, have eluded efforts to develop efficient inference algorithms. We show that probabilistic inference using belief networks is NP-hard. Therefore, it seems unlikely that an exact algorithm can be developed to perform probabilistic inference efficiently over all classes of belief networks. This result suggests that research should be directed away from the search for a general, efficient probabilistic inference algorithm, and toward the design of efficient special-case, average-case, and approximation algorithms.

Representing the negotiation process with a rule-based formalism

The objective of this paper is to introduce a flexible approach to the structuring of negotiations. The process of negotiations with its intricacies is discussed, and drawbacks of quantitative methods are analyzed. The decomposition of the negotiation process into a certain hierarchical structure is presented. This structure is represented with ‘and/or’ trees used for knowledge representation in artificial intelligence. The definitions of flexibility and reactions to the opponent's moves are introduced with the help of a rule-based formalism. The implications of these definitions for the analysis of the negotiation process are presented. The approach is illustrated with a set of hypothetical examples.

Self-organization in a perceptual network

The emergence of a feature-analyzing function from the development rules of simple, multilayered networks is explored. It is shown that even a single developing cell of a layered network exhibits a remarkable set of optimization properties that are closely related to issues in statistics, theoretical physics, adaptive signal processing, the formation of knowledge representation in artificial intelligence, and information theory. The network studied is based on the visual system. These results are used to infer an information-theoretic principle that can be applied to the network as a whole, rather than a single cell. The organizing principle proposed is that the network connections develop in such a way as to maximize the amount of information that is preserved when signals are transformed at each processing stage, subject to certain constraints. The operation of this principle is illustrated for some simple cases. >

Mental Spaces from a Functional Perspective

In his book Mental Spaces, Fauconnier develops a powerful theory of human knowledge representation and linguistic processing that handles a variety of problems in linguistics and the philosophy of language in a simple, uniform, and intuitively plausible way. However, he has little to say about the structure or general role of mental spaces in cognition. The present paper proposes that mental spaces are a means of organizing knowledge in support of a general inference method, simulative reasoning, found in various guises both in logic and in Artificial Intelligence. The structuring required to fulfill this role allows us to make a wide variety of predictions which seem to be borne out by evidence from natural language. In attributing a specific function to mental spaces, this paper suggests that the theory of mental spaces defines a potentially significant paradigm for knowledge representation in Artificial Intelligence.