An overview of Knowledge Representation

Domain descriptions should represent more than the characteristics of data and the operations on it. They should be "semantic" in the sense that they may represent information such as the meanings of special terms used in the business, as well as goals and rules. ER models are often described as "semantic data models". However, the correspondence between ER and natural language is through syntactic rather than through semantic constructs. Conceptual modeling languages and knowledge representation techniques are more appropriate for representing domain meaning. Modern research in linguistics, semantics, and artificial intelligence provides valuable insight into basic issues regarding such representations. Domain descriptions must use languages based on generally-accepted linguistic and knowledge representation principles.

Semantic relations are an important element in the construction of ontologies and models of problem domains. Nevertheless, they remain fuzzy or under-specified. This is a pervasive problem in software engineering and artificial intelligence. Thus, we find semantic links that can have multiple interpretations in wide-coverage ontologies, semantic data models with abstractions that are not enough to capture the relation richness of problem domains, and improperly structured taxonomies. However, if relations are provided with precise semantics, some of these problems can be avoided, and meaningful operations can be performed on them. In this paper we present some insightful issues about the modeling, representation and usage of relations including the available taxonomy structuring methodologies as well as the initiatives aiming to provide relations with precise semantics. Moreover, we explain and propose the control of relations as a key issue for the coherent construction of ontologies.

Commonsense reasoning is in principle a central problem in artificial intelligence, but it is a very difficult one. One approach that has been pursued since the earliest days of the field has been to encode commonsense knowledge as statements in a logic-based representation language and to implement commonsense reasoning as some form of logical inference. This paper surveys the use of logic-based representations of commonsense knowledge in artificial intelligence research.

This report describes an approach to modelling conversation. It is suggested that to succeed at this endeavour, the problem must be tackled principally as a problem in pragmatics rather than as one in language analysis alone. Several progmatic aspects of conversation are delineated and it is shown that the attempt to account for them raises a number of general issues in the representation of knowledge. .br A scheme for resolving some of these issues is presented and given computational description as a set of (non-implemented) LISP-based control structures called $\mid$LISP. Central to this scheme are several different types of objects that encode knowledge and communicate this knowledge by passing messages. One particular kind of object, the pattern expression ($\mid$PEXPR), turns out to be the most versatile. $\mid$PEXPRs can encode an arbitrary amount of procedural or declarative information; are capable, as a by-product of their message passing behaviour, of providing both a context for future processing decisions and a record of past processing decisions; and make contributions to the resolution of several artificial intelligence problems. .br Some examples of typical conversations that might occur in the general context of attending a symphony concert are then explored, and a particular model of conversation to handle these examples is detailed in $\mid$LISP. The model is goal oriented in its behaviour, and, in fact, is described in terms of four main goal levels: higher level non-linguistic goals; scripts directing both sides of a conversation; speech acts guiding one conversant''s actions; and, finally, language level goals providing a basic parsing component for the model. In addition, a place is delineated for belief models of the conversants, necessary if utterances are to be properly understood or produced. The embedding of this kind of language model in a $\mid$LISP base yields a rich pragmatic environment for analyzing conversation.

Chapter 1 Knowledge Representation and Classical Logic

Learning and planning in partially observable environments without prior domain knowledge

Artificial intelligence research started with computers trying to take the place of what humans have to do intelligently. Research started in the 1950s went through several ups and downs and reached the same period of the third boom as it is now. With the shocking situation where Alpha Go defeated Lee Se-dol, the strongest player in Go, in 2016, interest in artificial intelligence increased rapidly, and stories about the future situations that artificial intelligence will bring about, such as the possibility and singularity of artificial intelligence, are talked about even among the general public. Certainly, artificial intelligence has the potential to fundamentally change the way humans live, not just technological advances. The real value of artificial intelligence algorithms is prediction. At the center of all the algorithmic revolutions that have taken place in the various fields of modern society, there is one unchanging purpose: prediction. The algorithm for predicting the risk of recidivism used in US law enforcement fits precisely with the principle that individual humans behave in a predictable and consistent manner. While using the algorithm of predicting the risk of a second offender, an artificial intelligence algorithm, not a judge, actually takes the place of the defendant s decision on whether or not to be arrested or sentenced. Automated decision making continues to grow at an unprecedented rate and benefits because machine learning offers the possibility to extend the automated decision-making process, allowing wider, deeper decisions without human intervention. It is also very large and is expected to continue to expand its use. However, the risks are as great as the benefits of artificial intelligence algorithms. The risks of artificial intelligence algorithms outweigh the individual risks of any algorithmic technology specifically applied. Despite the widespread use of artificial intelligence in the social sphere, the reality is that the problems of artificial intelligence cannot be properly grasped. However, people who were treated differently due to automatic decision-making by artificial intelligence began to question the fairness or fairness of artificial intelligence, and in particular, while causing sensitive problems such as discriminatory results, controversy surrounding artificial intelligence algorithms gradually became more and more controversial. It is expanding. Even if a decision made by artificial intelligence is wrong or causes negative effects such as discrimination, there is a big problem that there are few means to object to this. In a situation where automated decision-making is more likely to threaten human autonomy and dignity, in order to secure an individual s autonomous personality, make sure that the entire process of the individual s automatic decision-making that affects him or her is fair and error-free. It is of utmost importance to know and allow humans to directly intervene if necessary. Although available data is increasing and algorithms are gradually improving, artificial intelligence algorithms do not completely eliminate the uncertainty posed by decision making. Eventually, there are cases in which humans, as finalists, have to make important decisions in situations where uncertainty has not been resolved. It is necessary to build a system in which artificial intelligence algorithms and human experts cooperate well to make the best decisions, and in addition to the direction of such a collaboration system establishment, the problem of legal responsibility due to errors in decision-making in artificial intelligence algorithms or collaboration systems is necessary. I want to deal with it in this paper.

This chapter discusses architectural aspects of Distributed Knowledge Base Management Systems. We investigate how research results on knowledge representation from the artificial intelligence side and on massive data management from the database side may be effectively combined in the design and development of a future distributed knowledge base management system architecture. In particular we address the incorporation of preexisting, autonomously created and administered, heterogeneous database systems as components in a Distributed Knowledge Base Management System and we illustrate how a relational data base schema may be translated into a semantic data model using the Temporal Hierarchic Model [FN85, SCHI83b] as the representation tool.KeywordsKnowledge RepresentationDatabase AdministratorDatabase SchemaLocal DatabaseData DictionaryThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

A theoretical issue that has always been central to research in artificial intelligence is the concept of representation. For the sake of simplicity, one may say that analysis of the matter has moved in two main directions: the symbolic direction summed up by research in knowledge representation, and the sub-symbolic direction of connectionist analysis.

The goal of this paper is to establish that there exists a strong relationship between current issues of data models in Database Management and representations of knowledge in Artificial Intelligence. The different data modelling techniques that have been used in the two research areas are surveyed and classified into Predicate Calculus-based, Network, and Procedural ones. The similarities and differences between them are presented.

The Application of Algorithmic Probability to Problems in Artificial Intelligence

This study attempted to present discussions and directions regarding artificial intelligence(AI) and the prevention of corruption in Korean real estate. This Paper dealt with moral hazard, corruption prevention, and trust using real estate, the 4th industrial revolution(4IR), AI, and intelligent information technology as keywords. Prior research is focusing on economic utility and technical benefits related to AI and real estate. In contrast, this study pointed out the risk of moral hazard and corruption arising from the combination of AI and real estate, and conducted a paper to prevent moral hazard and corruption. In this respect, there is a difference between this study and existing previous studies. This is where the originality of this study appears. The results of this study on anti-corruption of AI and Korean real estate are as follows. As a result of the study, the problems of AI and Korean real estate, and anti-corruption were presented in three major categories: 1. AI and the problem of changing times, 2. Korean real estate and corruption problem, and 3. AI and real estate problem. In relation to this, this study largely derived three measures as implications of the research. The contents include, first, responses to the issues of AI and changing times, second, responses to Korean real estate and corruption issues, and third, measures to respond to AI and real estate issues. This study hopes to restore market trust through Artificial Intelligence(AI) and the prevention of corruption in Korean real estate, and to build a future where the benefits and utility provided by real estate, AI, and intelligent information technology are desirable.

With the rapid development and progress of the times, many new things have been produced, and some problem solving measures have been updated and iterated. As a communication tool, mobile phones have also been upgraded in all aspects, such as face recognition, voiceprint recognition, etc., which are highlighting the research field of today’s world’s attention - artificial intelligence. The emergence of artificial intelligence is a combination of science and technology and law, and its application is very wide-ranging. In the face of its extensive application space, it has brought many legal problems, so it has attracted great attention in academia and practice, and the shortcomings of artificial intelligence include the absolute rationalization of algorithms, etc., and the legal problems brought about by it are one of the focuses of this paper. When there are certain problems in artificial intelligence, people’s desire for the law to properly regulate it will intensify, so how to properly regulate the problems caused by artificial intelligence through law is also the focus of this article. Postmodernism is a new term, whose etymology derives from modernism, and it is the result of dissatisfaction with modernism. In postmodernism, jurists have shown their views on artificial intelligence from a new perspective, contributing to the legal issues brought about by artificial intelligence, because of their thinking, people have a more comprehensive understanding of artificial intelligence issues along this thinking angle. For artificial intelligence, the research is a good example of the hot issue previously studied by the legal community - the legal subject of artificial intelligence from the perspective of legal philosophy. Professionals give the public some ways to think and benign angles for their reference, and they will have a more thorough understanding of artificial intelligence.

That artificial intelligence (AI), algorithms, and related technologies could use a few good booster shots of “humanism” is widely apparent. In both program code and implementation, AI and algorithms have been accused of harbouring deep-seated flaws that conflict with human values. They are prime examples of the skew towards white, Western, men and demonstrate the bankruptcy in the face of neoliberalist, profit- and market-oriented social paradigms that this special issue seeks to address. Currently, computer scientists and AI researchers who are looking to remedy these problems are often in favour of more data, more powerful machines, more complex algorithms—in short, that we should fix problems with AI by building better AI. In this view human beings and the world can be modelled in code—our lives, interactions, society, and our very selves can be broken down into data points which can be assessed by highly advanced technologies. When these scientists and researchers seek to broaden their approach they often look to philosophy. However, the philosophy they look to is overwhelmingly Anglo-analytic, which views the world in extremely similar ways. Both AI and Anglo-analytic philosophy argue for solutions to humanistic problems which are essentially mathematical. They share in seeing important concepts, such as persons, emotions, agency, and ethics, as mechanistic, atomistic, and calculable. In this paper I will argue that Classical Chinese philosophy offer insightful resources for addressing the humanist problems in AI. Rather than arguing for mathematical solutions, or envisioning persons, emotions, agency, and ethics, as other rigid, atomistic, and mechanistic approaches, Chinese philosophy emphasizes transformation, interrelatedness, and correlative developments. Accordingly, it offers tools for appreciating the world, society, and ourselves as spontaneous, complex, and full of tension. AI can be programmed and used in ways that do not reduce the complexity and conflict in the world, but provide us instead with tools to make sense of it—tools that are humanistic in nature. To this end, Chinese philosophy can be a helpful collaborative partner.

Twenty years of AI research in knowledge representation has produced frame knowledge representation systems (FRSs) that incorporate a number of important advances. However, FRSs lack two important capabilities that prevent them from scaling up to realistic applications: they cannot provide high-speed access to large knowledge bases (KBs), and they do not support shared, concurrent KB access by multiple users. Our research investigates the hypothesis that one can employ an existing database management system (DBMS) as a storage subsystem for an FRS, to provide high-speed access to large, shared KBs. We describe the design and implementation of a general storage system that incrementally loads referenced frames from a DBMS, and saves modified frames back to the DBMS, for two different FRSs: LOOM and THEO. We also present experimental results showing that the performance of our prototype storage subsystem exceeds that of flat files for simulated applications that reference or update up to one third of the frames from a large LOOM KB.