What to Read: A Biased Guide to AI Literacy for the Beginner

Acknowledgements. It was Ken Forbus' idea, and he, Howie Shrobe, Dan Weld, and John Batali read various drafts. Dan Huttenlocher and Tom Knight helped with the speech recognition section. The science fiction section was prepared with the aid of my SF/AI editorial board, consisting of Carl Feynman and David Wallace, and of the ArpaNet SF-Lovers community. Even so, all responsibility rests with me.This note tries to provide a quick guide to AI literacy for the beginning AI hacker and for the experienced AI hacker or two whose scholarship isn't what it should be. most will recognize it as the same old list of classic papers, give or take a few that I feel to be under- or over-rated. It is not guaranteed to be thorough or balanced or anything like that.MIT Artificial Intelligence Laborator

Evaluation von Wissensrepräsentationssystemen

Ziel dieses Berichtes ist eine Evaluation von aktuellen Wissensrepräsentationssystemen, insbesondere terminologischen Logiken. Nach Aufstellung der relevanten Evaluationskriterien erfolgt zunächst eine allgemeine Behandlung von KL-ONE - der Urmutter der terminologischen Logiken -, wobei schon einige inhärente kritische Punkte der zu behandelnden Systeme aufgezeigt werden. Anschließend werden Syntax- und Semantikdefinitionen von KL-ONE-Derivaten vorgestellt, um deren Sprachumfang zu vergleichen. Neben den gängigen KL-ONE-Derivaten wird auch die in LILOG verwendete Repräsentationssprache vorgestellt. Abschließend erfolgt ein zusammenfassender Vergleich der Systeme. Hierbei stellt sich heraus, dass insbesondere die Systeme LOOM, CLASSIC, KRIS und BACK bezüglich des verwendeten Sprachumfangs und der Effizienz der Inferenzen gut abschneiden. Die Systeme BACK und KRIS sind dabei für Verbmobil besonders relevant, da sie relativ leicht verfügbar sind. Außerdem zeichnet sich BACK durch ein gut strukturiertes Handbuch aus und eine schnelle neue Implementierung In C. Kritisch bei allen vorgestellten Systemen ist die Darstellung zeitlicher Zusammenhänge (Ereignisse, Aktionen); hierzu liegen jedoch schon Forschungsergebnisse für die Erweiterung der terminologischen Sprachen vor

Psychology

Although the discipline of psychology, in its contemporary form, is only a century old, psychology\u27s historical antecedents reach back to the beginnings of civilization. Whether defined as the study of the soul or the study of human faculties, as it was in earlier times, or as the study of consciousness, mind, or behavior, as it has been over the past hundred years, psychology has dealt with some of the fundamental questions and issues pertaining to the functions, processes, and mechanisms of human and animal nature

Meinongian Semantics and Artificial Intelligence

This essay describes computational semantic networks for a philosophical audience and surveys several approaches to semantic-network semantics. In particular, propositional semantic networks (exemplified by SNePS) are discussed; it is argued that only a fully intensional, Meinongian semantics is appropriate for them; and several Meinongian systems are presented

Control, Multiple Description, and Purpose in the Visual Perception of Complex Scenes: A Pogress Report

This report describes research done at the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for the laboratory's artificial intelligence research is provided in part by the Advanced Research Projects Agency of the Department of Defense under Office of Naval Research contract N00014-75-C-0643.This memo describes a vision program for recognizing simple furniture comprising assemblies of blocks, in which the same item may be composed in diverse ways. As such, it is concerned with three theoretical issues, perceptual processing, supression of unwanted detail, and segregation and interconnection of information. The program's perceptual processing relies on an elaborate, redundant, alterable model of the scene rather than on any clever process structure. This approach aids the interpretation of incomplete, ambiguous portions of the scene as well as simplifies the program. The model is capable of quantitative as well as qualitative alteration, by a constraint-propogation system and a system of frame-shift demons. The hierarchical nature of the scene - assemblies of assemblies of blocks - is reflected as hierarchy in the model. Each assembly is represented as having an external aspect, by which it relates to surrounding assemblies, and an internal aspect, listing the parts and relationships composing it. This imposes a natural supression of detail. In addition to the vertical layering of the model there are horizontal subdivisions adapted for different computational purposes. There is a 2D section representing the image, a 3D section representing the shape, and a stability section representing the physical forces and moments acting upon each unit. Each of the sections can be used through any of several indirect reference frames corresponding to different spatial viewpoints. Many computations on the model, such as stability analysis, spatial relationships, and visual matching, are greatly simplified by first selecting the proper spatial viewpoints.MIT Artificial Intelligence Laborator

APPLICATION AND REFINEMENTS OF THE REPS THEORY FOR SAFETY CRITICAL SOFTWARE

With the replacement of old analog control systems with software-based digital control systems, there is an urgent need for developing a method to quantitatively and accurately assess the reliability of safety critical software systems. This research focuses on proposing a systematic software metric-based reliability prediction method. The method starts with the measurement of a metric. Measurement results are then either directly linked to software defects through inspections and peer reviews or indirectly linked to software defects through empirical software engineering models. Three types of defect characteristics can be obtained, namely, 1) the number of defects remaining, 2) the number and the exact location of the defects found, and 3) the number and the exact location of defects found in an earlier version. Three models, Musa's exponential model, the PIE model and a mixed Musa-PIE model, are then used to link each of the three categories of defect characteristics with reliability respectively. In addition, the use of the PIE model requires mapping defects identified to an Extended Finite State Machine (EFSM) model. A procedure that can assist in the construction of the EFSM model and increase its repeatability is also provided. This metric-based software reliability prediction method is then applied to a safety-critical software used in the nuclear industry using eleven software metrics. Reliability prediction results are compared with the real reliability assessed by using operational failure data. Experiences and lessons learned from the application are discussed. Based on the results and findings, four software metrics are recommended. This dissertation then focuses on one of the four recommended metrics, Test Coverage. A reliability prediction model based on Test Coverage is discussed in detail and this model is further refined to be able to take into consideration more realistic conditions, such as imperfect debugging and the use of multiple testing phases

A neural network and rule based system application in water demand forecasting

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel University.This thesis describes a short term water demand forecasting application that is based upon a combination of a neural network forecast generator and a rule based system that modifies the resulting forecasts. Conventionally, short term forecasting of both water consumption and electrical load demand has been based upon mathematical models that aim to either extract the mathematical properties displayed by a time series of historical data, or represent the causal relationships between the level of demand and the key factors that determine that demand. These conventional approaches have been able to achieve acceptable levels of prediction accuracy for those days where distorting, non cyclic influences are not present to a significant degree. However, when such distortions are present, then the resultant decrease in prediction accuracy has a detrimental effect upon the controlling systems that are attempting to optimise the operation of the water or electricity supply network. The abnormal, non cyclic factors can be divided into those which are related to changes in the supply network itself, those that are related to particular dates or times of the year and those which are related to the prevailing meteorological conditions. If a prediction system is to provide consistently accurate forecasts then it has to be able to incorporate the effects of each of the factor types outlined above. The prediction system proposed in this thesis achieves this by the use of a neural network that by the application of appropriately classified example sets, can track the varying relationship between the level of demand and key meteorological variables. The influence of supply network changes and calendar related events are accounted for by the use of a rule base of prediction adjusting rules that are built up with reference to past occurrences of similar events. The resulting system is capable of eliminating a significant proportion of the large prediction errors that can lead to non optimal supply network operation