In another example of disruption through AI, travel companies have begun using behavioral data and predictive analytics to customize brand experiences based on individuals' preferences and patterns. Automating IT functions alone reduces expenses by 14 to 28 percent, so companies that launch using automated services quickly establish a financial advantage over larger, legacy-burdened competitors. Some tech experts believe that the current generation of applied AI systems, such as predictive analytics, will give small businesses advantages through increased automation and efficiency. New BI platforms offer data visualization, customer relationship management programs, and other critical BI services.
Thimm, Matthias (Universität Koblenz-Landau) | Villata, Serena (Laboratoire d'Informatique, Signaux et Systèmes de Sophia-Antipolis (I3S)) | Cerutti, Federico (Cardiff University) | Oren, Nir (University of Aberdeen) | Strass, Hannes (Leipzig University) | Vallati, Mauro (University of Huddersfield)
We review the First International Competition on Computational Models of Argumentation (ICMMA'15). The competition evaluated submitted solvers performance on four different computational tasks related to solving abstract argumentation frameworks. Each task evaluated solvers in ways that pushed the edge of existing performance by introducing new challenges. Despite being the first competition in this area, the high number of competitors entered, and differences in results, suggest that the competition will help shape the landscape of ongoing developments in argumentation theory solvers.
A system for learning concept descriptions incrementally is described and illustrated by a series of experiments in the domains of insect classification, chess endgames and plant disease diagnosis. The system employs a full-memory learning method that incrementally improves hypotheses, but does not forget facts. The method is used to form both characteristic descriptions, which describe a concept in detail, and discriminant descriptions, which specify only properties needed to distinguish a given concept from a given set of other concepts. Experimental results show the advantages of inducing and maintaining only characteristic descriptions during learning and creating discriminant descriptions from them when a classification decision is necessary. Research in the area of concept learning from examples has been concerned mainly with methods for single step, or non-incremental, learning.
In particular its task domain is the analysis of mass spectra, chemical data gathered routinely from a relatively new analytical instrument, the mass spectrometer. This collaboration of chemists and computer scientists has produced what appears to be an interesting program from the viewpoint of artificial intelligence and a useful tool from the viewpoint of chemistry. For this discussion it is sufficient to say that a mass spectrometer is an instrument into which is put a minute sample of some chemical compound and out of which comes data usually represented as a bar graph. This is what is referred to here as the mass spectrum. The x-points of the bar graph represent the masses of ions produced and the y-points represent the relative abundances of ions of these masses.
I AUTOMATED REASONING: LOGICAL AND HEURISTIC 1 2 WHAT ARE EXPERT SYSTEMS? 3 HISTORICAL BACKGROUND The idea of automated reasoning is founded on the fact that computers are general-purpose symbol manipulation devices, and not mere numerical calculating machines. Symbolic inference since the time of Aristotle has involved the combination of symbolic expressions. One line of attack on the problem of how to use computers for automated reasoning is the logical one: exploit the syntax and rules of deductive logic as expressed by Aristotle, Russell & Whitehead, or Church Extend the formalism where necessary to represent useful concepts not easily expressed. But focus sharply on retaining the logical consistency that these formalisms provide. A primary research problem is finding computational methods that are efficient ent,,,gh for this theorem-proving approach to be applied to reasoning problems of real-world complexity.
Jeffrey S. Rosenschein and Vineet Singh Heuristic Programming Project Computer Science Department Stanford University Stanford, CA 94305 Abstract Meta-level control, in an Artificial Intelligence system, can provide increased capabilities This improvement, however, is achieved at the cost of the meta-level effort itself. This paper outlines a formalization of the costs involved in choosing between independent problem-solving methods: the cost of meta-level control is explicitly included. It is often desirable for Artificial Intelligence systems to make use of explicit knowledge about what they know; this tneta-level knowledge allows a program to direct its own activities in an informed and efficient manner [I] [21. The use of meta-level knowledge by a system to control its own actions is called'new-level confrol. If we are to gain efficiene; thi-migh the use of meta-level effort, \'.e must be sure that %'.hat is aved at the base level is not canceled by what is expended at the rnota-level.
I. INTRODUCTION Since November 1979, a group at the Information Sciences Institute of the University of Southern California has been working on ell implementation of Interlisp for the DEC VAX-scries1 computers. 'Ibis report is a description of the current status, future prospects, and estimated character of that Interlisp-VAX implementation. It is the result of several days of discussion with those at ISI involved with the implementation (Dave Dyer, Hans Koomen, Ray Bates. Dan Lynch); with John L. White of MIT, who is working on an implementation of another Lisp for the VAX (NIL); with'Ibis document has been revised as a result of comments received. In early April 1981, a meeting of ARPA-sponsored or related Lisp users was held at SRI, to discuss the status and future of Lisp.
Edward H. Shortliffe, Jul 1981 HP? -81 -9 EVALUATINq EXPERT SYSTEMS Edward H. Shortliffe Heuristic Programming Project Departments of Medicine and Computer Science Stanford University Stanford, California 94305 July 1981 This paper is the author's contribution to Chapter 6. in the volume EXPERT SYSTEMS, edited by R. Hayes-Roth, D. Lenat, and D. Waterman:4 The full article is entitled "Evaluation of expert systems: issues and case studies", and is authored by J. Gaschnig, P. Klahr, H. Pople, E. Shortliffe. The volume is the result of a Workshop on Expert Systems held in San Diego in August 1980 and sponsored by the Rand Corporation, ARPA, and the NSF. Parts of Chapters 7 & 8. Reprinted with permission. Issues in the Evaluation of Expert Systems EVALUATING EXPERT SYSTEMS 1 Issues in the Evaluation of Expert Systems 4e have been discussing the reasons for doing evaluations of expert systems, or for having reservations about getting involved in the evaluation process, but we have not addressed the nature of the evaluation process itself. In this section we define ma,ly of the parameters that determine an appropriate design for an evaluation experiment.
Knowledge engineering is still more of an art than a science. In the list presented here, we have tried to capture the art as it exists at the beginning of the 1980's. Many of his heuristics still apply.) 'David Barstow is a member of the Schlumberger-Doll Research Laboratory, Ridgefield, CT. This memo is also being published by SDR as Al Memo number 10. 1 In the course of building expert systems, knowledge engineers have developed intuitions about how best to proceed, heuristics to keep in mind when building a system for a particular task.
Reprinted by permission of the Canadian Society for Computational Studies of Intelligence. Reprinted frcm: Proceedings of the CSCSI/SCEIO Conference 14-16 May 1980 University of Victoria Victoria, British Columbia pp. California 94305 ABSTFACT Computer systems for use by physicians have had limited impact on clinical medicine. The mYCIN System is used to illustrate tne ways In which Our research group has attempted to respond to the design criteria cited. My goal is to present design criteria which may encourage the use of computer programs oy physicians, and to show that Al offers some particularly pertinent methods for responding to the design criteria outlined.