APPROXIMATE KNOWLEDGE OF MANY AGENTS AND DISCOVERY SYSTEMS
|
|
- Cecil Perkins
- 5 years ago
- Views:
Transcription
1 Jan M. Żytkow APPROXIMATE KNOWLEDGE OF MANY AGENTS AND DISCOVERY SYSTEMS 1. Introduction Automated discovery systems have been growing rapidly throughout 1980s as a joint venture of researchers in artificial intelligence (AI), and (to a lesser degree) in philosophy of science [1]; [3]. Many systems have been constructed and applied, primarily to various re-discovery tasks. The theory of heuristic search guided the work, but this theory applies to just about everything in AI, while there has been little specific theoretical foundation for automated discovery. On April 14th, 1989, Professor Rasiowa gave a lecture at George Mason University on Poset-based Logics of Approximate Reasoning. The lecture linked subjects of Epistemic Logics, Information Systems, Semi- Post Algebras, and Rough Sets. The common thread has been sets of agents, arranged in partial orders and lattices. One of the key notions has been a predicate d t p i, by which agent t approximates predicate p i. As expected, the lecture has been dominated by formal systems, their basic properties and semantics. As a former philosopher, who for many years engaged in discussions on epistemic logics, knowledge operators and approximate reasoning, I have been sceptical of their practical use in application to human mind which notoriously eludes portrayals in formal theories. It seems hard to expect human conformity to formal assumptions of a system of logic. Suddenly I realized the potential for applications in the domain of computer discovery systems, my main area of research. A long discussion on the following day brought more light on the use of logics for multi-agent systems. Unlike humans, computer systems can be inspected in every detail. They can be also altered to meet formal requirements of a given system of logic or algebra. Theory of multiple agents can enter the stage in various ways. Different versions of one system can be considered as agents, who 185
2 form a set partially ordered by the capabilities available in each version. Different parts of a system can be viewed as individual agents, too. This interpretation is natural in multiprocess systems. The apparatus of logic can be used externally to the computer system, for instance by the developer, to reason about properties of the system, or to improve the design. But it is also possible and more attractive to put multi-agent logics to internal use by the systems, so that agents actually reason by means of a given logic or algebra. Many further encounters with logics for approximate knowledge followed. Especially important for me has been a number of meetings with Professor Rauszer at the Warsaw Banach Center in the Fall She contributed generously her time and advice, exploring the links between rough set, multi-agent logics and automated discovery. My personal impression has been that Helena Rasiowa, Cecylia Rauszer, Andrzej Skowron, and other logicians, as well as the inventor of rough sets Zdzislaw Pawlak have been intrigued and even thrilled by the possible applications of their mathematical work in discovery systems. At present, the applications have been external, helping to organize the thinking of the developer and aiding the design of multi-agent systems. But a larger impact of their visionary work will crop up in the future internal applications. That task is more difficult, because it requires operationalization of the logic formalism. We need decision procedures and proof mechanisms whenever available. We also need a number of extensions which I discuss at the end. Builders of automated discovery systems resemble constructors of gothic cathedrals; possessed builders, whose ambition is to construct bigger and more capable structures. Surely the cathedral builders knew the laws of statics, but the theory trailed far behind practice. The builders push for theory advancements when no higher buildings can be raised. The constructors of discovery systems will soon face limitations of their artifacts and seek new theories, among those the new logic foundations. 2. Many agents in a discovery system Rasiowa, Rauszer and their collaborators (e.g., [HR75], [CR47]) focused primarily on agents who can approximate a predicate or a set. We can call 186
3 them measuring agents. We will focus on such agents and at the end we will briefly explore the role for theoretical agents. Empirical inquiry includes experimentation with the physical world. Here discovery systems offer measuring agents the role of empirical semantics. Measuring agents are needed to make direct links between the system working on the computer and the physical world. Automation of those links is enabled by variety of hardware (manipulators and sensors) designed for scientific laboratories and furnished with computer interfaces. Manipulators such as burets, heaters, and valves or sensors such as balances, thermometers, and ph meters are plentiful. They allow robotic systems to perform a vast range of scientific experiments. Taking advantage of new robotic hardware, in recent years we have developed a number of robotic discoverers. Some make chemistry experiments and require no moving parts ([4], 1992), while other take on the form of mobile robots and robot arms. What part of a robotic system can be treated as a measuring agent? The hardware of a particular instrument is necessary but not sufficient. Hardware must be driven by a piece of software called device driver. Individual measurements of instruments controlled by device drivers, however, are rarely sufficient as scientific data. To measure a magnitude that characterizes a true physical property of objects in an experiment setup S, the sensing must be adjusted to the specifics of S and its environment. This often requires combined use of several sensors and manipulators, guided by an operational definition, that is an algorithm that controls many elementary actions of sensors and manipulators. Only jointly they lead to justified measurements that interpret a physical property. In every experiment setup there is room for improved accuracy of actions and measurements, by construction of more adequate operational definitions. This is a discovery process. Both the setup S and operational definitions can be re-arranged with the help of empirical regularities, discovered in S for the earlier versions of operational definitions. In conclusion, a measuring agent is defined by a combination of operational definition, device drivers, and instruments. Such agents make up the necessary physical interpretation of physical properties and facts for the automated discoverer. Unlike formal semantics defined in metamathematics, this is a non-formal definition. Little can be proved about each interpretation, because no formal structure is assumed on the part of semantics. The semantics, however, can be evaluated empirically, and should satisfy the requirements of stability of readings, minimality of measurement 187
4 error, and generality of the scope of measurements. All these virtues are reflected in the quality of the subsequently discovered knowledge. 3. A closer look at measuring agents Why is it useful to have many agents available for a single physical magnitude? Why not use only the best agent, who may translate to an ideal in the lattice of agents? There are several reasons. Often there is no best one among available agents, as their ranges of application overlap. For instance, each balance applies only in a specific range of situations. Many agents, whose readings are less accurate, possess advantages of greater stability. This is why it is appealing to represent a set of procedures by a partially ordered set of agents or by a lattice. Since each agent comes with its own indiscernibility relation, rough logic [CR47] is a natural application. Different relations combine according to the rules of rough set theory. The ideals, even if physically unavailable, can be mathematically defined, and they make practical sense, too. They may, for instance, denote the limitations of discernibility for a given repertoire of empirical procedures and instruments. A multiprocess system makes the multi-agent perspective particularly appealing and useful, because different agents can be identified with different processes. In robotic discoverers multiprocessing is used for practical purposes. Some sensor readings must be taken in parallel, some other may take a long period of time. A single process approach may suffer inefficiency, when it must wait repeatedly for sensor readings. A specific approximation logic [HR75] or rough logic for many agents [CR47] can help in many ways, guiding our reasoning about measuring agents, helping in the design of multi-agent collaboration and in seeking a unified theory of operational procedures. But in addition, a given logic and a given partial ordering may be used in the actual reasoning by the agents. 4. New challenges Measuring agents, as represented in the current framework of rough logic, are limited in many ways. Equivalence relation, interpreted as indistinguishability, should be replaced by tolerance, which seems empirically more 188
5 appropriate and fundamental. Recent work that expands the notion of rough set to rough functions and operations on rough functions (Pawlak, 1995) meets many problems but can aid automation of discovery. Manipulating agents who control the empirical setup are as necessary as measuring agents. Equally needed are theoretical agents who use results of measurements and manipulations to discover regularities and to reason about them. The algebraic approach to logic, proposed in semi-post algebras and expanded by Rasiowa and Rauszer to multi-agent systems, can be a very useful guide to reasoning about knowledge by theoretical agents. Reasoning about knowledge adds new dimensions to reasoning about facts handled by today s rough logic or alternative approaches such as statistics. Acknowledgments: Helena Rasiowa, Cecylia Rauszer, Andrzej Jankowski, Zdzislaw Pawlak and Andrzej Skowron made significant contributions to the new perspective on discovery systems outlined in this paper. References [1] P. Langley, H. A. Simon, G. Bradshaw and J. M. Żytkow, Scientific Discovery: Computational Explorations of the Creative Processes. Cambridge, MA: MIT Press [2] Z. Pawlak, On Rough Derivatives, Rough Integrals, and Rough Differential Equations, ICS Research Report 41/95, Warsaw University of Technology [3] J. Shrager and P. Langley (eds.), Computational Models of Scientific Discovery and Theory Formation, San Mateo, CA: Morgan Kaufmann [4] J. M. Żytkow, J. Zhu and R. Zembowicz, Operational Definition Refinement: a Discovery Process, Proceedings of the Tenth National Conference on Artificial Intelligence, The AAAI Press, 1992, pp Computer Science Department Wichita State University; Wichita, KS USA & Institute of Computer Science, Polish Academy of Sciences 189
ROBOT-DISCOVERER: A ROLE MODEL FOR ANY INTELLIGENT AGENT. and Institute of Computer Science, Polish Academy of Sciences.
ROBOT-DISCOVERER: A ROLE MODEL FOR ANY INTELLIGENT AGENT JAN M. _ ZYTKOW Department of Computer Science, UNC Charlotte, Charlotte, NC 28223, USA and Institute of Computer Science, Polish Academy of Sciences
More informationRobot-discoverer: artiæcial intelligent agent who searches for. knowledge. Jan M. _ Zytkow. Department of Computer Science
Robot-discoverer: artiæcial intelligent agent who searches for knowledge Jan M. _ Zytkow zytkow@uncc.edu Department of Computer Science University of North Carolina, Charlotte, NC 28223 U.S.A. Abstract
More informationElectrical and Automation Engineering, Fall 2018 Spring 2019, modules and courses inside modules.
Electrical and Automation Engineering, Fall 2018 Spring 2019, modules and courses inside modules. Period 1: 27.8.2018 26.10.2018 MODULE INTRODUCTION TO AUTOMATION ENGINEERING This module introduces the
More informationIntelligent Systems. Lecture 1 - Introduction
Intelligent Systems Lecture 1 - Introduction In which we try to explain why we consider artificial intelligence to be a subject most worthy of study, and in which we try to decide what exactly it is Dr.
More informationAwareness and Understanding in Computer Programs A Review of Shadows of the Mind by Roger Penrose
Awareness and Understanding in Computer Programs A Review of Shadows of the Mind by Roger Penrose John McCarthy Computer Science Department Stanford University Stanford, CA 94305. jmc@sail.stanford.edu
More informationPAPER. Connecting the dots. Giovanna Roda Vienna, Austria
PAPER Connecting the dots Giovanna Roda Vienna, Austria giovanna.roda@gmail.com Abstract Symbolic Computation is an area of computer science that after 20 years of initial research had its acme in the
More informationIntroduction to Computer Science - PLTW #9340
Introduction to Computer Science - PLTW #9340 Description Designed to be the first computer science course for students who have never programmed before, Introduction to Computer Science (ICS) is an optional
More informationIntroduction to Artificial Intelligence: cs580
Office: Nguyen Engineering Building 4443 email: zduric@cs.gmu.edu Office Hours: Mon. & Tue. 3:00-4:00pm, or by app. URL: http://www.cs.gmu.edu/ zduric/ Course: http://www.cs.gmu.edu/ zduric/cs580.html
More informationDetecticon: A Prototype Inquiry Dialog System
Detecticon: A Prototype Inquiry Dialog System Takuya Hiraoka and Shota Motoura and Kunihiko Sadamasa Abstract A prototype inquiry dialog system, dubbed Detecticon, demonstrates its ability to handle inquiry
More informationOutline. What is AI? A brief history of AI State of the art
Introduction to AI Outline What is AI? A brief history of AI State of the art What is AI? AI is a branch of CS with connections to psychology, linguistics, economics, Goal make artificial systems solve
More informationComputational Thinking for All
for All Corporate Vice President, Microsoft Research Consulting Professor of Computer Science, Carnegie Mellon University Centrality and Dimensions of Computing Panel Workshop on the Growth of Computer
More informationCOMP219: Artificial Intelligence. Lecture 2: AI Problems and Applications
COMP219: Artificial Intelligence Lecture 2: AI Problems and Applications 1 Introduction Last time General module information Characterisation of AI and what it is about Today Overview of some common AI
More informationLaboratory 1: Uncertainty Analysis
University of Alabama Department of Physics and Astronomy PH101 / LeClair May 26, 2014 Laboratory 1: Uncertainty Analysis Hypothesis: A statistical analysis including both mean and standard deviation can
More informationArtificial Intelligence
Artificial Intelligence Chapter 1 Chapter 1 1 Outline What is AI? A brief history The state of the art Chapter 1 2 What is AI? Systems that think like humans Systems that think rationally Systems that
More informationArtificial Intelligence
Artificial Intelligence Chapter 1 Chapter 1 1 Outline What is AI? A brief history The state of the art Chapter 1 2 What is AI? Systems that think like humans Systems that think rationally Systems that
More informationFirst steps towards a mereo-operandi theory for a system feature-based architecting of cyber-physical systems
First steps towards a mereo-operandi theory for a system feature-based architecting of cyber-physical systems Shahab Pourtalebi, Imre Horváth, Eliab Z. Opiyo Faculty of Industrial Design Engineering Delft
More informationCMSC 421, Artificial Intelligence
Last update: January 28, 2010 CMSC 421, Artificial Intelligence Chapter 1 Chapter 1 1 What is AI? Try to get computers to be intelligent. But what does that mean? Chapter 1 2 What is AI? Try to get computers
More information3 A Locus for Knowledge-Based Systems in CAAD Education. John S. Gero. CAAD futures Digital Proceedings
CAAD futures Digital Proceedings 1989 49 3 A Locus for Knowledge-Based Systems in CAAD Education John S. Gero Department of Architectural and Design Science University of Sydney This paper outlines a possible
More informationHOW CAN CAAD TOOLS BE MORE USEFUL AT THE EARLY STAGES OF DESIGNING?
HOW CAN CAAD TOOLS BE MORE USEFUL AT THE EARLY STAGES OF DESIGNING? Towards Situated Agents That Interpret JOHN S GERO Krasnow Institute for Advanced Study, USA and UTS, Australia john@johngero.com AND
More informationCS:4420 Artificial Intelligence
CS:4420 Artificial Intelligence Spring 2018 Introduction Cesare Tinelli The University of Iowa Copyright 2004 18, Cesare Tinelli and Stuart Russell a a These notes were originally developed by Stuart Russell
More information15: Ethics in Machine Learning, plus Artificial General Intelligence and some old Science Fiction
15: Ethics in Machine Learning, plus Artificial General Intelligence and some old Science Fiction Machine Learning and Real-world Data Ann Copestake and Simone Teufel Computer Laboratory University of
More informationCOMPETITIVE ADVANTAGES AND MANAGEMENT CHALLENGES. by C.B. Tatum, Professor of Civil Engineering Stanford University, Stanford, CA , USA
DESIGN AND CONST RUCTION AUTOMATION: COMPETITIVE ADVANTAGES AND MANAGEMENT CHALLENGES by C.B. Tatum, Professor of Civil Engineering Stanford University, Stanford, CA 94305-4020, USA Abstract Many new demands
More informationDesign and Technology Subject Outline Stage 1 and Stage 2
Design and Technology 2019 Subject Outline Stage 1 and Stage 2 Published by the SACE Board of South Australia, 60 Greenhill Road, Wayville, South Australia 5034 Copyright SACE Board of South Australia
More informationENTRY ARTIFICIAL INTELLIGENCE
ENTRY ARTIFICIAL INTELLIGENCE [ENTRY ARTIFICIAL INTELLIGENCE] Authors: Oliver Knill: March 2000 Literature: Peter Norvig, Paradigns of Artificial Intelligence Programming Daniel Juravsky and James Martin,
More informationDevelopment of a Laboratory Kit for Robotics Engineering Education
Development of a Laboratory Kit for Robotics Engineering Education Taskin Padir, William Michalson, Greg Fischer, Gary Pollice Worcester Polytechnic Institute Robotics Engineering Program tpadir@wpi.edu
More informationThis list supersedes the one published in the November 2002 issue of CR.
PERIODICALS RECEIVED This is the current list of periodicals received for review in Reviews. International standard serial numbers (ISSNs) are provided to facilitate obtaining copies of articles or subscriptions.
More informationUnderstanding Coevolution
Understanding Coevolution Theory and Analysis of Coevolutionary Algorithms R. Paul Wiegand Kenneth A. De Jong paul@tesseract.org kdejong@.gmu.edu ECLab Department of Computer Science George Mason University
More informationFormal Verification. Lecture 5: Computation Tree Logic (CTL)
Formal Verification Lecture 5: Computation Tree Logic (CTL) Jacques Fleuriot 1 jdf@inf.ac.uk 1 With thanks to Bob Atkey for some of the diagrams. Recap Previously: Linear-time Temporal Logic This time:
More informationCSC 550: Introduction to Artificial Intelligence. Fall 2004
CSC 550: Introduction to Artificial Intelligence Fall 2004 See online syllabus at: http://www.creighton.edu/~davereed/csc550 Course goals: survey the field of Artificial Intelligence, including major areas
More informationCreating Scientific Concepts
Creating Scientific Concepts Nancy J. Nersessian A Bradford Book The MIT Press Cambridge, Massachusetts London, England 2008 Massachusetts Institute of Technology All rights reserved. No part of this book
More informationCreative Design. Sarah Fdili Alaoui
Creative Design Sarah Fdili Alaoui saralaoui@lri.fr Outline A little bit about me A little bit about you What will this course be about? Organisation Deliverables Communication Readings Who are you? Presentation
More informationArtificial Intelligence
Artificial Intelligence Chapter 1 Chapter 1 1 Outline What is AI? A brief history The state of the art Chapter 1 2 What is AI? Systems that think like humans Systems that think rationally Systems that
More informationA Three Cycle View of Design Science Research
Scandinavian Journal of Information Systems Volume 19 Issue 2 Article 4 2007 A Three Cycle View of Design Science Research Alan R. Hevner University of South Florida, ahevner@usf.edu Follow this and additional
More informationPower System Dynamics and Control Prof. A. M. Kulkarni Department of Electrical Engineering Indian institute of Technology, Bombay
Power System Dynamics and Control Prof. A. M. Kulkarni Department of Electrical Engineering Indian institute of Technology, Bombay Lecture No. # 25 Excitation System Modeling We discussed, the basic operating
More informationKeywords Multi-Agent, Distributed, Cooperation, Fuzzy, Multi-Robot, Communication Protocol. Fig. 1. Architecture of the Robots.
1 José Manuel Molina, Vicente Matellán, Lorenzo Sommaruga Laboratorio de Agentes Inteligentes (LAI) Departamento de Informática Avd. Butarque 15, Leganés-Madrid, SPAIN Phone: +34 1 624 94 31 Fax +34 1
More informationUsing Dynamic Capability Evaluation to Organize a Team of Cooperative, Autonomous Robots
Using Dynamic Capability Evaluation to Organize a Team of Cooperative, Autonomous Robots Eric Matson Scott DeLoach Multi-agent and Cooperative Robotics Laboratory Department of Computing and Information
More informationIntroduction to AI. What is Artificial Intelligence?
Introduction to AI Instructor: Dr. Wei Ding Fall 2009 1 What is Artificial Intelligence? Views of AI fall into four categories: Thinking Humanly Thinking Rationally Acting Humanly Acting Rationally The
More informationIowa State University Library Collection Development Policy Computer Science
Iowa State University Library Collection Development Policy Computer Science I. General Purpose II. History The collection supports the faculty and students of the Department of Computer Science in their
More informationWelcome to EGN-1935: Electrical & Computer Engineering (Ad)Ventures
: ECE (Ad)Ventures Welcome to -: Electrical & Computer Engineering (Ad)Ventures This is the first Educational Technology Class in UF s ECE Department We are Dr. Schwartz and Dr. Arroyo. University of Florida,
More informationAppendices master s degree programme Artificial Intelligence
Appendices master s degree programme Artificial Intelligence 2015-2016 Appendix I Teaching outcomes of the degree programme (art. 1.3) 1. The master demonstrates knowledge, understanding and the ability
More informationMaster Artificial Intelligence
Master Artificial Intelligence Appendix I Teaching outcomes of the degree programme (art. 1.3) 1. The master demonstrates knowledge, understanding and the ability to evaluate, analyze and interpret relevant
More informationAGENT PLATFORM FOR ROBOT CONTROL IN REAL-TIME DYNAMIC ENVIRONMENTS. Nuno Sousa Eugénio Oliveira
AGENT PLATFORM FOR ROBOT CONTROL IN REAL-TIME DYNAMIC ENVIRONMENTS Nuno Sousa Eugénio Oliveira Faculdade de Egenharia da Universidade do Porto, Portugal Abstract: This paper describes a platform that enables
More informationTowards a novel method for Architectural Design through µ-concepts and Computational Intelligence
Towards a novel method for Architectural Design through µ-concepts and Computational Intelligence Nikolaos Vlavianos 1, Stavros Vassos 2, and Takehiko Nagakura 1 1 Department of Architecture Massachusetts
More informationCPS331 Lecture: Intelligent Agents last revised July 25, 2018
CPS331 Lecture: Intelligent Agents last revised July 25, 2018 Objectives: 1. To introduce the basic notion of an agent 2. To discuss various types of agents Materials: 1. Projectable of Russell and Norvig
More informationThe Science In Computer Science
Editor s Introduction Ubiquity Symposium The Science In Computer Science The Computing Sciences and STEM Education by Paul S. Rosenbloom In this latest installment of The Science in Computer Science, Prof.
More informationComputer Science and Philosophy Information Sheet for entry in 2018
Computer Science and Philosophy Information Sheet for entry in 2018 Artificial intelligence (AI), logic, robotics, virtual reality: fascinating areas where Computer Science and Philosophy meet. There are
More informationSubmitted November 19, 1989 to 2nd Conference Economics and Artificial Intelligence, July 2-6, 1990, Paris
1 Submitted November 19, 1989 to 2nd Conference Economics and Artificial Intelligence, July 2-6, 1990, Paris DISCOVERING AN ECONOMETRIC MODEL BY. GENETIC BREEDING OF A POPULATION OF MATHEMATICAL FUNCTIONS
More informationAppendices master s degree programme Human Machine Communication
Appendices master s degree programme Human Machine Communication 2015-2016 Appendix I Teaching outcomes of the degree programme (art. 1.3) 1. The master demonstrates knowledge, understanding and the ability
More informationTraffic Control for a Swarm of Robots: Avoiding Group Conflicts
Traffic Control for a Swarm of Robots: Avoiding Group Conflicts Leandro Soriano Marcolino and Luiz Chaimowicz Abstract A very common problem in the navigation of robotic swarms is when groups of robots
More informationArtificial Intelligence
Artificial Intelligence Chapter 1 Chapter 1 1 Outline Course overview What is AI? A brief history The state of the art Chapter 1 2 Administrivia Class home page: http://inst.eecs.berkeley.edu/~cs188 for
More informationCo-evolution of agent-oriented conceptual models and CASO agent programs
University of Wollongong Research Online Faculty of Informatics - Papers (Archive) Faculty of Engineering and Information Sciences 2006 Co-evolution of agent-oriented conceptual models and CASO agent programs
More informationLecture 13: Requirements Analysis
Lecture 13: Requirements Analysis 2008 Steve Easterbrook. This presentation is available free for non-commercial use with attribution under a creative commons license. 1 Mars Polar Lander Launched 3 Jan
More informationA Model-Theoretic Approach to the Verification of Situated Reasoning Systems
A Model-Theoretic Approach to the Verification of Situated Reasoning Systems Anand 5. Rao and Michael P. Georgeff Australian Artificial Intelligence Institute 1 Grattan Street, Carlton Victoria 3053, Australia
More informationThe attribution problem in Cognitive Science. Thinking Meat?! Formal Systems. Formal Systems have a history
The attribution problem in Cognitive Science Thinking Meat?! How can we get Reason-respecting behavior out of a lump of flesh? We can t see the processes we care the most about, so we must infer them from
More information5.4 Imperfect, Real-Time Decisions
5.4 Imperfect, Real-Time Decisions Searching through the whole (pruned) game tree is too inefficient for any realistic game Moves must be made in a reasonable amount of time One has to cut off the generation
More informationChapter 7 Information Redux
Chapter 7 Information Redux Information exists at the core of human activities such as observing, reasoning, and communicating. Information serves a foundational role in these areas, similar to the role
More informationZolt-Gilburne Imagination Seminar. Knowledge and Games. Sergei Artemov
Zolt-Gilburne Imagination Seminar Knowledge and Games Sergei Artemov October 1, 2009 1 Plato (5-4 Century B.C.) One of the world's best known and most widely read and studied philosophers, a student of
More informationEvoCAD: Evolution-Assisted Design
EvoCAD: Evolution-Assisted Design Pablo Funes, Louis Lapat and Jordan B. Pollack Brandeis University Department of Computer Science 45 South St., Waltham MA 02454 USA Since 996 we have been conducting
More informationJournal Title ISSN 5. MIS QUARTERLY BRIEFINGS IN BIOINFORMATICS
List of Journals with impact factors Date retrieved: 1 August 2009 Journal Title ISSN Impact Factor 5-Year Impact Factor 1. ACM SURVEYS 0360-0300 9.920 14.672 2. VLDB JOURNAL 1066-8888 6.800 9.164 3. IEEE
More informationArtificial Intelligence: An overview
Artificial Intelligence: An overview Thomas Trappenberg January 4, 2009 Based on the slides provided by Russell and Norvig, Chapter 1 & 2 What is AI? Systems that think like humans Systems that act like
More informationEngineering, & Mathematics
8O260 Applied Mathematics for Technical Professionals (R) 1 credit Gr: 10-12 Prerequisite: Recommended prerequisites: Algebra I and Geometry Description: (SGHS only) Applied Mathematics for Technical Professionals
More informationApplication Areas of AI Artificial intelligence is divided into different branches which are mentioned below:
Week 2 - o Expert Systems o Natural Language Processing (NLP) o Computer Vision o Speech Recognition And Generation o Robotics o Neural Network o Virtual Reality APPLICATION AREAS OF ARTIFICIAL INTELLIGENCE
More informationIntelligent Agents. Introduction to Planning. Ute Schmid. Cognitive Systems, Applied Computer Science, Bamberg University. last change: 23.
Intelligent Agents Introduction to Planning Ute Schmid Cognitive Systems, Applied Computer Science, Bamberg University last change: 23. April 2012 U. Schmid (CogSys) Intelligent Agents last change: 23.
More informationCourse Syllabus. P age 1 5
Course Syllabus Course Code Course Title ECTS Credits COMP-263 Human Computer Interaction 6 Prerequisites Department Semester COMP-201 Computer Science Spring Type of Course Field Language of Instruction
More informationThe popular conception of physics
54 Teaching Physics: Inquiry and the Ray Model of Light Fernand Brunschwig, M.A.T. Program, Hudson Valley Center My thinking about these matters was stimulated by my participation on a panel devoted to
More informationApplication of Definitive Scripts to Computer Aided Conceptual Design
University of Warwick Department of Engineering Application of Definitive Scripts to Computer Aided Conceptual Design Alan John Cartwright MSc CEng MIMechE A thesis submitted in compliance with the regulations
More informationCSTA K- 12 Computer Science Standards: Mapped to STEM, Common Core, and Partnership for the 21 st Century Standards
CSTA K- 12 Computer Science s: Mapped to STEM, Common Core, and Partnership for the 21 st Century s STEM Cluster Topics Common Core State s CT.L2-01 CT: Computational Use the basic steps in algorithmic
More informationPlayware Research Methodological Considerations
Journal of Robotics, Networks and Artificial Life, Vol. 1, No. 1 (June 2014), 23-27 Playware Research Methodological Considerations Henrik Hautop Lund Centre for Playware, Technical University of Denmark,
More informationIMPROVEMENTS TO A QUEUE AND DELAY ESTIMATION ALGORITHM UTILIZED IN VIDEO IMAGING VEHICLE DETECTION SYSTEMS
IMPROVEMENTS TO A QUEUE AND DELAY ESTIMATION ALGORITHM UTILIZED IN VIDEO IMAGING VEHICLE DETECTION SYSTEMS A Thesis Proposal By Marshall T. Cheek Submitted to the Office of Graduate Studies Texas A&M University
More informationAC Measurement of Magnetic Susceptibility
AC Measurement of Magnetic Susceptibility Ferromagnetic materials such as iron, cobalt and nickel are made up of microscopic domains in which the magnetization of each domain has a well defined orientation.
More informationFundamentals of Industrial Control
Fundamentals of Industrial Control 2nd Edition D. A. Coggan, Editor Practical Guides for Measurement and Control Preface ix Contributors xi Chapter 1 Sensors 1 Applications of Instrumentation 1 Introduction
More informationEmotional BWI Segway Robot
Emotional BWI Segway Robot Sangjin Shin https:// github.com/sangjinshin/emotional-bwi-segbot 1. Abstract The Building-Wide Intelligence Project s Segway Robot lacked emotions and personality critical in
More informationBricken Technologies Corporation Presentations: Bricken Technologies Corporation Corporate: Bricken Technologies Corporation Marketing:
TECHNICAL REPORTS William Bricken compiled 2004 Bricken Technologies Corporation Presentations: 2004: Synthesis Applications of Boundary Logic 2004: BTC Board of Directors Technical Review (quarterly)
More informationTropes and Facts. onathan Bennett (1988), following Zeno Vendler (1967), distinguishes between events and facts. Consider the indicative sentence
URIAH KRIEGEL Tropes and Facts INTRODUCTION/ABSTRACT The notion that there is a single type of entity in terms of which the whole world can be described has fallen out of favor in recent Ontology. There
More informationCPS331 Lecture: Agents and Robots last revised April 27, 2012
CPS331 Lecture: Agents and Robots last revised April 27, 2012 Objectives: 1. To introduce the basic notion of an agent 2. To discuss various types of agents 3. To introduce the subsumption architecture
More informationResearch Statement MAXIM LIKHACHEV
Research Statement MAXIM LIKHACHEV My long-term research goal is to develop a methodology for robust real-time decision-making in autonomous systems. To achieve this goal, my students and I research novel
More informationLogical Agents (AIMA - Chapter 7)
Logical Agents (AIMA - Chapter 7) CIS 391 - Intro to AI 1 Outline 1. Wumpus world 2. Logic-based agents 3. Propositional logic Syntax, semantics, inference, validity, equivalence and satifiability Next
More information11/18/2015. Outline. Logical Agents. The Wumpus World. 1. Automating Hunt the Wumpus : A different kind of problem
Outline Logical Agents (AIMA - Chapter 7) 1. Wumpus world 2. Logic-based agents 3. Propositional logic Syntax, semantics, inference, validity, equivalence and satifiability Next Time: Automated Propositional
More informationMODALITY, SI! MODAL LOGIC, NO!
MODALITY, SI! MODAL LOGIC, NO! John McCarthy Computer Science Department Stanford University Stanford, CA 94305 jmc@cs.stanford.edu http://www-formal.stanford.edu/jmc/ 1997 Mar 18, 5:23 p.m. Abstract This
More informationCENTER OF BASICS SCIENCE ELECTRONIC ENGINEER (Curriculum 2012)
OBJECTIVE To form professionals in the electronics engineer field in order to design, implement and keep digital and computer systems, automation systems and mechatronics and communications systems, supporting
More informationUNIT 2 TOPICS IN COMPUTER SCIENCE. Emerging Technologies and Society
UNIT 2 TOPICS IN COMPUTER SCIENCE Emerging Technologies and Society EMERGING TECHNOLOGIES Technology has become perhaps the greatest agent of change in the modern world. While never without risk, positive
More informationJournal of Professional Communication 3(2):41-46, Professional Communication
Journal of Professional Communication Interview with George Legrady, chair of the media arts & technology program at the University of California, Santa Barbara Stefan Müller Arisona Journal of Professional
More informationDigital image processing vs. computer vision Higher-level anchoring
Digital image processing vs. computer vision Higher-level anchoring Václav Hlaváč Czech Technical University in Prague Faculty of Electrical Engineering, Department of Cybernetics Center for Machine Perception
More informationEmbodiment: Does a laptop have a body?
Embodiment: Does a laptop have a body? Pei Wang Temple University, Philadelphia, USA http://www.cis.temple.edu/ pwang/ Abstract This paper analyzes the different understandings of embodiment. It argues
More informationIndiana K-12 Computer Science Standards
Indiana K-12 Computer Science Standards What is Computer Science? Computer science is the study of computers and algorithmic processes, including their principles, their hardware and software designs,
More informationOutline. Introduction to AI. Artificial Intelligence. What is an AI? What is an AI? Agents Environments
Outline Introduction to AI ECE457 Applied Artificial Intelligence Fall 2007 Lecture #1 What is an AI? Russell & Norvig, chapter 1 Agents s Russell & Norvig, chapter 2 ECE457 Applied Artificial Intelligence
More informationarxiv: v1 [cs.ai] 20 Feb 2015
Automated Reasoning for Robot Ethics Ulrich Furbach 1, Claudia Schon 1 and Frieder Stolzenburg 2 1 Universität Koblenz-Landau, {uli,schon}@uni-koblenz.de 2 Harz University of Applied Sciences, fstolzenburg@hs-harz.de
More informationTwo Perspectives on Logic
LOGIC IN PLAY Two Perspectives on Logic World description: tracing the structure of reality. Structured social activity: conversation, argumentation,...!!! Compatible and Interacting Views Process Product
More informationInternational Journal of Research in Advent Technology Available Online at:
OVERVIEW OF DIFFERENT APPROACHES OF PID CONTROLLER TUNING Manju Kurien 1, Alka Prayagkar 2, Vaishali Rajeshirke 3 1 IS Department 2 IE Department 3 EV DEpartment VES Polytechnic, Chembur,Mumbai 1 manjulibu@gmail.com
More informationTitle? Alan Turing and the Theoretical Foundation of the Information Age
BOOK REVIEW Title? Alan Turing and the Theoretical Foundation of the Information Age Chris Bernhardt, Turing s Vision: the Birth of Computer Science. Cambridge, MA: MIT Press 2016. xvii + 189 pp. $26.95
More informationAI Principles, Semester 2, Week 1, Lecture 2, Cognitive Science and AI Applications. The Computational and Representational Understanding of Mind
AI Principles, Semester 2, Week 1, Lecture 2, Cognitive Science and AI Applications How simulations can act as scientific theories The Computational and Representational Understanding of Mind Boundaries
More informationDesign Science Research Methods. Prof. Dr. Roel Wieringa University of Twente, The Netherlands
Design Science Research Methods Prof. Dr. Roel Wieringa University of Twente, The Netherlands www.cs.utwente.nl/~roelw UFPE 26 sept 2016 R.J. Wieringa 1 Research methodology accross the disciplines Do
More informationThe first topic I would like to explore is probabilistic reasoning with Bayesian
Michael Terry 16.412J/6.834J 2/16/05 Problem Set 1 A. Topics of Fascination The first topic I would like to explore is probabilistic reasoning with Bayesian nets. I see that reasoning under situations
More informationDevelopment of motor body fixture using blackboard framework approch
Development of motor body fixture using blackboard framework approch Mr. A. D. PARSANA M.E.[Machine Design] Student, Department Of Mechanical Engineering, R. K. College Of Engineering And Technology, Rajkot,
More informationFRONT END INNOVATION Multidisciplinary innovation process
FRONT END INNOVATION Multidisciplinary innovation process CONTENT Front end innovation process Multidisciplinary innovation FRONT END AS A PART OF PRODUCT DEVELOPMENT PROCESS Business planning Production
More informationWhat is AI? AI is the reproduction of human reasoning and intelligent behavior by computational methods. an attempt of. Intelligent behavior Computer
What is AI? an attempt of AI is the reproduction of human reasoning and intelligent behavior by computational methods Intelligent behavior Computer Humans 1 What is AI? (R&N) Discipline that systematizes
More information- Basics of informatics - Computer network - Software engineering - Intelligent media processing - Human interface. Professor. Professor.
- Basics of informatics - Computer network - Software engineering - Intelligent media processing - Human interface Computer-Aided Engineering Research of power/signal integrity analysis and EMC design
More informationDesign of intelligent surveillance systems: a game theoretic case. Nicola Basilico Department of Computer Science University of Milan
Design of intelligent surveillance systems: a game theoretic case Nicola Basilico Department of Computer Science University of Milan Outline Introduction to Game Theory and solution concepts Game definition
More information4D-Particle filter localization for a simulated UAV
4D-Particle filter localization for a simulated UAV Anna Chiara Bellini annachiara.bellini@gmail.com Abstract. Particle filters are a mathematical method that can be used to build a belief about the location
More informationOptimal Rhode Island Hold em Poker
Optimal Rhode Island Hold em Poker Andrew Gilpin and Tuomas Sandholm Computer Science Department Carnegie Mellon University Pittsburgh, PA 15213 {gilpin,sandholm}@cs.cmu.edu Abstract Rhode Island Hold
More information