Discovering qualitative empirical laws

In this paper we describe GLAUBER, an AI system that models the scientific discovery of qualitative empirical laws. We have tested the system on data from the history of early chemistry, and it has rediscovered such concepts as acids, alkalis, and salts, as well as laws relating these concepts. After discussing GLAUBER we examine the program's relation to other discovery systems, particularly methods for conceptual clustering and language acquisition

Neural Networks for Predicting Algorithm Runtime Distributions

Many state-of-the-art algorithms for solving hard combinatorial problems in artificial intelligence (AI) include elements of stochasticity that lead to high variations in runtime, even for a fixed problem instance. Knowledge about the resulting runtime distributions (RTDs) of algorithms on given problem instances can be exploited in various meta-algorithmic procedures, such as algorithm selection, portfolios, and randomized restarts. Previous work has shown that machine learning can be used to individually predict mean, median and variance of RTDs. To establish a new state-of-the-art in predicting RTDs, we demonstrate that the parameters of an RTD should be learned jointly and that neural networks can do this well by directly optimizing the likelihood of an RTD given runtime observations. In an empirical study involving five algorithms for SAT solving and AI planning, we show that neural networks predict the true RTDs of unseen instances better than previous methods, and can even do so when only few runtime observations are available per training instance

Perceived trends in viewing the future by Croatian and Slovenian business students: Implications for managerial education

This paper presents appreciative inquiry (AI) methodology in the context of management education. Therefore, we propose that AI, which focuses on positive aspects of doing business, needs to be implemented into management education of future managers in Slovenia and Croatia and we provide empirical evidence of thinking patterns of business students studying at the University of Ljubljana and the University of Split. For comparison, we investigated the previous experiences and future expectations of business students in Croatia and Slovenia. Empirical part is composed of two parts: qualitatively based AI and a quantitative study, based on statistical methods. Results show a positive outlook of business students in both countries

The challenge of complexity for cognitive systems

Complex cognition addresses research on (a) high-level cognitive processes – mainly problem solving, reasoning, and decision making – and their interaction with more basic processes such as perception, learning, motivation and emotion and (b) cognitive processes which take place in a complex, typically dynamic, environment. Our focus is on AI systems and cognitive models dealing with complexity and on psychological findings which can inspire or challenge cognitive systems research. In this overview we first motivate why we have to go beyond models for rather simple cognitive processes and reductionist experiments. Afterwards, we give a characterization of complexity from our perspective. We introduce the triad of cognitive science methods – analytical, empirical, and engineering methods – which in our opinion have all to be utilized to tackle complex cognition. Afterwards we highlight three aspects of complex cognition – complex problem solving, dynamic decision making, and learning of concepts, skills and strategies. We conclude with some reflections about and challenges for future research

AI in Law: How Artificial Intelligence Is Transforming the Legal Profession in Indonesia

The profess on the development of Artificial Intelligence (AI) has widely transformed a new era in the digital technology, social economic, human need and professional behavior. Eventually, artificial intelligence automates even more aspects of legal profession. Furthermore, AI allows the legal profession to automate lower-level tasks, freeing time to focus on complex analysis and client interaction. The research aims to know the literacy of legal professional on the use of AI. The methods used in this study are normative-empirical legal research. Furthermore, research uses primary data which obtained from questionary survey, secondary data i.e., law, books, journals and other related legal sources for research. Meanwhile, the research will analyze through descriptive qualitatively. The research shows that the literacy level of law enforcers in the use of AI is in the medium category. Data shows that 75% of legal professional shows their positive respond about the implementation of AI in their profession. While other shows, 8% of high percentage and 17% of low percentage