Planning as Artificial Intelligence Problem - Short Introduction and Overview

Question answering (QA) is a very important, but not yet completely resolved problem in artificial intelligence. Solving the QA problem consists of two major steps: relevant fact selection and answering the question. Existing methods usually combine the two steps to solve the problem. A major technique is to add a memory component to infer answers from the chaining facts. It is not very clear how irrelevant facts affect the effectiveness of these methods. In this paper, we propose to separate the two steps and only consider the problem of relevant fact selection. We used a graphical probabilistic model Conditional Random Field (CRF) to model the interdependent relationship among the chaining facts in order to select the relevant ones. In our experiments on a benchmark dataset, we are able to select correctly all relevant facts from 13 tasks out of 19 tasks (F-scores of the rest of the 6 tasks range from 0.8 to 0.97). We also show that using our selector to pre-select relevant facts can substantially improve the accuracies of existing QA systems (e.g. MemN2N (from 88% to 94%) and LSTM (from 66% to 91%) in 13 tasks with complete information).

The inductive learning methodology known as decision trees, concerns the ability to classify objects based on their attributes values, using a tree like structure from which decision rules can be accrued. In this article, a description of decision trees is given, with the main emphasis on their operation in a fuzzy environment. A first reference to decision trees is made in Hunt et al. (1966), who proposed the Concept learning system to construct a decision tree that attempts to minimize the score of classifying chess endgames. The example problem concerning chess offers early evidence supporting the view that decision trees are closely associated with artificial intelligence (AI). It is over ten years later that Quinlan (1979) developed the early work on decision trees, to introduced the Interactive Dichotomizer 3 (ID3). The important feature with their development was the use of an entropy measure to aid the decision tree construction process (using again the chess game as the considered problem). It is ID3, and techniques like it, that defines the hierarchical structure commonly associated with decision trees, see for example the recent theoretical and application studies of Pal and Chakraborty (2001), Bhatt and Gopal (2005) and Armand et al. (2007). Moreover, starting from an identified root node, paths are constructed down to leaf nodes, where the attributes associated with the intermediate nodes are identified through the use of an entropy measure to preferentially gauge the classification certainty down that path. Each path down to a leaf node forms an ‘if .. then ..’ decision rule used to classify the objects. The introduction of fuzzy set theory in Zadeh (1965), offered a general methodology that allows notions of vagueness and imprecision to be considered. Moreover, Zadeh’s work allowed the possibility for previously defined techniques to be considered with a fuzzy environment. It was over ten years later that the area of decision trees benefited from this fuzzy environment opportunity (see Chang and Pavlidis, 1977). Since then there has been a steady stream of research studies that have developed or applied fuzzy decision trees (FDTs) (see recently for example Li et al., 2006 and Wang et al., 2007). The expectations that come with the utilisation of FDTs are succinctly stated by Li et al. (2006, p. 655); “Decision trees based on fuzzy set theory combines the advantages of good comprehensibility of decision trees and the ability of fuzzy representation to deal with inexact and uncertain information.” Chiang and Hsu (2002) highlight that decision trees has been successfully applied to problems in artificial intelligence, pattern recognition and statistics. They go onto outline a positive development the FDTs offer, namely that it is better placed to have an estimate of the degree that an object is associated with each class, often desirable in areas like medical diagnosis (see Quinlan (1987) for the alternative view with respect to crisp decision trees). The remains of this article look in more details at FDTs, including a tutorial example showing the rudiments of how an FDT can be constructed.

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.

Nowadays, many real problem in Artificial Intelligence can be modeled as constraint satisfaction problems (CSPs). A general rule in constraint satisfaction is to tackle the hardest part of a search problem first. In this paper, we introduce a parameter (τ) that measures the constrainedness of a search problem. This parameter represents the probability of the problem being feasible. A value of τ= 0 corresponds to an over-constrained problem and no states are expected to be solutions. A value of τ=1 corresponds to an under-constrained problem which every state is a solution. This parameter can also be used in a heuristic to guide search. To achieve this parameter, a sample in finite population is carried out to compute the tightnesses of each constraint. We take advantage of this tightnesses to classify the constraints from the tightest constraint to the loosest constraint. This heuristic may accelerate the search due to inconsistencies can be found earlier and the number of constraint checks can significantly be reduced.KeywordsConstraint Satisfaction Problemscomplexityheuristic search

Logic programming (LP) has been successfully applied to solve many problems in artificial intelligence and many other areas. However, LP is unable to deal with uncertain, imprecise or vague information. On the other hand, fuzzy sets and fuzzy logic have demonstrated their applicability in dealing with uncertainty, and have motivated the research on extending classic LP to add fuzzy reasoning. However, a full fledged implementation of a fuzzy logic programming language universally accepted and/or in production does not exist yet. There are many reasons for this, and we present some of them. The aim of the paper is to briefly survey some of the major design problems we faced in implementing a fuzzy version of a Prolog-like logic programming language.

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.

The importance of the topic of the article is due to the fact that nowadays there is a need to understand that the creation of artificial intelligence is associated with the understanding and study of natural intelligence. The problem of defining artificial intelligence is largely reduced to defining intelligence in general: is intelligence monolithic or does this concept define a set of different abilities? To what extent intelligence can be created? Is it possible to create computers with intelligence? These and other questions have not yet been answered by the contemporary science, but these questions have greatly contributed to the formation of tasks and methodologies that form the basis of the theory and practice of contemporary artificial intelligence in philosophy. The purpose of the article is the need to study and substantiate the indicators of overall success in the development of artificial intelligence systems; proving that human psychoreality is an open system of nonlinear type; proving the effectiveness of logo and psycho-machines in the development of artificial intelligence in philosophy. The article gives a theoretical justification for the concepts of “artificial intelligence” and “logo and psycho machines”; the conditions for the effectiveness of the development of artificial intelligence in our time are highlighted, as the scientific direction of artificial intelligence is young, and its structure and range of issues are not so clearly defined. It is now possible to implement formal systems of reasoning in the machine and test their sufficiency for the manifestation of reasonableness in practice.

Contemporary interdisciplinary researches are now based upon modern information technologies. The core of the studies at the Russian Academy of Sciences is the program of artificial intelligence. The research program of artificial intelligence represents an attempt to unite a conglomerate of different disciplines connected by a common task – to create a theoretical model of “natural” intelligence and to develop, if possible, the way (or “technology”) to manufacture it in order to apply it in different areas of human activity where “natural” intelligence is now used. However, such a program, in a strict sense of the term, still hardly exists – rather it is a “vague” ideology of complex research, which would be able to solve the set of various tasks by means of modern engineering and various “cognitive sciences”. Nevertheless, the Learned Council on the problem of artificial intelligence, which is focused on elaborating the program of interdisciplinary research of human reason with a special reference to artificial intelligence, was established at the beginning of 2006 under the guidance of the Presidium of the Russian Academy of Sciences. Let me remark, by the way, that not only leading Russian scientists but also the students of the corresponding institutes have been involved in the research work. The Council organized the conference entitled “Artificial Intelligence As the Subject of Interdisciplinary Research” (January, 2005) to which not only philosophers but also computer scientists, mathematicians, logicians etc. greatly contributed. The conference gave rise to a permanent seminar. It works with enormous success under the guidance of the vicepresident of the Russian Academy of Sciences. Nevertheless, very few people pose the question whether such a subject exists at all – even in the sense in which mathematicians speak about the “existence” of the solution of this or that problem (even though it still has not been found). In general, the progress of scientific knowledge, not only in mathematics but also in “empirical” sciences, often originates from the effort to solve such a problem, which has no scientific solutions or does not

Purpose. The study presents the key aspects of the introduction and use of artificial intelligence (AI) in the construction industry. The challenges faced by builders when implementing AI, such as the high cost of equipment and the need for retraining, are discussed. The advantages of AI are also considered, including improving design efficiency, reducing costs and improving control over construction processes. Special attention is paid to the use of AI in monitoring construction sites, risk forecasting, automation of typical operations and the use of autonomous technology. The article emphasizes that despite the existing problems, it is a powerful tool for the transformation of the construction industry, which can significantly improve the quality and speed of construction.Methods. The research uses methods of statistical and comparative analysis, the «golden square» method in the context of digitalization of the IC, focused on managing complex situations.Results. A classifier of threats and risks for IC in difficult situations has been developed from the point of view of the sustainable development of artificial intelligence in the construction industry.Conclusion. Artificial intelligence in construction opens up new horizons for improving efficiency and optimizing processes. From BIM technologies to autonomous robots and drones, and is becoming an integral part of the modern construction industry. However, despite all the advantages, there are also challenges, such as the high cost of equipment and the need for retraining. Nevertheless, the potential in construction is huge, and its implementation promises significant improvements in the quality, speed and safety of construction work. The paper provides an overview of the problems of IC in modern conditions. The analysis of the application of the "golden square" method in the context of digitalization of the IC, focused on managing complex situations, is presented. A classifier of threats and risks for the UK in difficult situations from the point of view of sustainable development has been developed.

The architecture of knowledge of computer systems and some problems of artificial intelligence (AI) are considered such as knowledge acquisition, knowledge processing, knowledge formation, etc.

The paper provides a general overview of current discussions on the phenomenal character of abstract thinking, which throws a fresh light on the problem of agency of artificial intelligence. Narrow and wide interpretations of cognitive experience phenomenal character as presented in contemporary discussions have been analyzed. According to those supporting the strong version of narrow interpretation of the phenomenal nature of cognitive experience, phenomenal properties are only characteristic ofthe sensor-perceptive realm of cognition, while mental experience has purely intentional, but not phenomenal character.The moderate version of narrow interpretation admits that abstract thinking can also have phenomenal character — due to its links with sensual and emotional states. Moderate position of mental experience’s narrow interpretation problematizes the very point of agency of artificial intelligence. Wide interpretation of the phenomenal character of cognition insists that abstract thinking also has phenomenal character: abstract thinking is characterized by «experiencing» a certain cognitive state, «being within» this cognitive state. In the framework of wide interpretation,the intentional character of mental experience seems not be reducible to sensual. The paper analyzes the ontological argument against wide interpretation of phenomenal experience as well as arguments (logical and metaphysical ones) in favor of the possibility of specifically mental phenomenal states. Discussions on cognitive phenomenology throw a fresh light both on the problems of artificial intelligence’s agency and on fundamental problems of epistemology in general. These include the problems of perceptual intentionality and its influence on high-level cognition processes, the qualitative character of mental experience, and the relevance of introspection to abstract thinkingstudies. General conclusion, if abstract thinking has its own phenomenal character, not reducible to phenomenal character of sensual experience, this undermines the foundations of computational cognitive approach and raises research on the problems of artificial intelligence to a higher level.

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.

Diversity is often announced as a solution to ethical problems in artificial intelligence (AI), but what exactly is meant by diversity and how it can solve those problems is seldom spelled out. This lack of clarity is one hurdle to motivating diversity in AI. Another hurdle is that while the most common perceptions about what diversity is are too weak to do the work set out for them, stronger notions of diversity are often defended on normative grounds that fail to connect to the values that are important to decision-makers in AI. However, there is a long history of research in feminist philosophy of science and a recent body of work in social epistemology that taken together provide the foundation for a notion of diversity that is both strong enough to do the work demanded of it, and can be defended on epistemic grounds that connect with the values that are important to decision-makers in AI. We clarify and defend that notion here by introducing emergent expertise as a network phenomenon wherein groups of workers with expertise of different types can gain knowledge not available to any individual alone, as long as they have ways of communicating across types of expertise. We illustrate the connected epistemic and ethical benefits of designing technology with diverse groups of workers using the examples of an infamous racist soap dispenser, and the millimeter wave scanners used in US airport security.