vanhelsing: A Fast Proof Checker for Debuggable Compiler Verification
|
|
- Berniece Rose
- 5 years ago
- Views:
Transcription
1 : A Fast Proof Checker for Debuggable Compiler Verification Roland Lezuo, Ioan Dragan :, Gergö Barany ;, Andreas Krall rlezuo@lang.tuwien.ac.at Vienna University of Technology, Institute of Comp Languages : Institute e-austria, Timisoara, Romania ; CEA LIST Software Reliability Laboratory, Gif-sur-Yvette, France September 2, This work is supported in part by the Austrian Research Promotion Agency (FFG) and by Catena DSP GmbH
2 Overview 2 3 R. Lezuo, I. Dragan, G. Barany, A. Krall Proof Checker
3 Used in Translation Validation Framework source p P SL pass pass 2 C ppq C 2 pc ppqq... C n p... C ppq...q pass n C n pc n p... C ppq...qq P TL binary R. Lezuo, I. Dragan, G. Barany, A. Krall Proof Checker
4 Per Pass Simulation Proofs ile p P L Cppq P L 2 L semantics (ASM) p L symbolic execution first-order formulas Æ Prover L 2 semantics (ASM) Cppq L2 symbolic execution first-order formulas Cf. : A Fast Proof Checker for Debuggable Compiler Verification (SYNASC 5) and Scalable Translation Validation, Ph.D. thesis, Roland Lezuo 204 R. Lezuo, I. Dragan, G. Barany, A. Krall Proof Checker
5 Requirements for Prover Evidence (traceable, constructive) Performance (prover dominates validation time) Debugging failed proofs (identify mis-ilation) Problem formulation (as-is, deal with it) R. Lezuo, I. Dragan, G. Barany, A. Krall Proof Checker
6 Comparison: Off-the-Shelf Provers Resolution based SMT Evidence Debugging Performance I ` Debugging matters: Tool used by iler experts Limited theorem proving knowledge Proofs are large, multiple thousands of formulas problem formulation dependent R. Lezuo, I. Dragan, G. Barany, A. Krall Proof Checker
7 Special Proof Structure sym = sym6 = Data-Flow Equivalence Problem (DFE) expression equivalence problem Visualization as trees (GraphViz) sym sym2 sym6 2 sym2 = = sym0 = unrelated sym0 sym8 = sym = sym sym2 sym2 = sym2 sym3 sym8 sym9 fof (id0, hypothesis, (sym,, sym2 )). fof (id, hypothesis, (sym2,, sym3 )). fof (id2, hypothesis, (sym3,sym4,sym5 )). fof (id3, hypothesis, (sym6,2, )). fof (id4, hypothesis, (,sym8,sym9 )). fof (id5, hypothesis, unrelated (sym0, sym, sym2 )). sym3 = sym4 = sym9 = sym3 sym4 sym5 sym5 = R. Lezuo, I. Dragan, G. Barany, A. Krall Proof Checker
8 Unification by a Forward Chaining Strategy Witness Information fof (op, hypothesis, sym=sym6 ). fof (op2, hypothesis, sym4=sym8 ). fof (cj, conjecture, sym5=sym9 ). Axioms of Transformations fof (ax,axiom,( (A,B,X) & (A,B,Y)) => X=Y). fof (ax2,axiom,( (A,,B) & (B,,C) & (A,2,D)) => C=D) sym6 = sym sym0 = sym = sym6 sym2 unrelated sym0 sym sym2 sym2 = sym6 2 sym2 = sym2 = sym3 sym8 = sym4 sym8 sym9 sym9 = sym5 R. Lezuo, I. Dragan, G. Barany, A. Krall Proof Checker
9 Debugging reduces Tree to relevant Subtrees start from pair of non-unified symbols of conjecture ute reachability (backwards) colorize nodes by membership of data-flows (i.e. left, right, both) sym6 = sym sym6 sym2 sym2 = sym6 2 = sym8 = sym4 sym5=sym9 sym2 sym3 sym8 sym9 sym3 = sym9 = sym3 sym8 sym5 sym5 = In practice reduces from thousands of formulas to a few dozen! R. Lezuo, I. Dragan, G. Barany, A. Krall Proof Checker
10 Performance Factor Vampire eprover Z isel.succ regalloc.succ vliw.succ vliw.fail isel.fail R. Lezuo, I. Dragan, G. Barany, A. Krall Proof Checker
11 Conclusion fast (up to factor 3) small (4k LOC) visual debugging Questions? R. Lezuo, I. Dragan, G. Barany, A. Krall Proof Checker
Hoare Logic and Model Checking. LTL and CTL: a perspective. Learning outcomes. Model Checking Lecture 12: Loose ends
Learning outcomes Hoare Logic and Model Checking Model Checking Lecture 12: Loose ends Dominic Mulligan Based on previous slides by Alan Mycroft and Mike Gordon Programming, Logic, and Semantics Group
More informationChapter 2. Knowledge Representation: Reasoning, Issues, and Acquisition. Teaching Notes
Chapter 2 Knowledge Representation: Reasoning, Issues, and Acquisition Teaching Notes This chapter explains how knowledge is represented in artificial intelligence. The topic may be launched by introducing
More informationLecture 2: Linear vs. Branching time. Temporal Logics: CTL, CTL*. CTL model checking algorithm. Counter-example generation.
CS 267: Automated Verification Lecture 2: Linear vs. Branching time. Temoral Logics: CTL, CTL*. CTL model checking algorithm. Counter-examle generation. Instructor: Tevfik Bultan Linear Time vs. Branching
More informationPart I - Multiple Choice. 10 points total.
Part I - Multiple Choice. 10 points total. Circle the one answer that best answers the question. 1. Which of the following is an effect axiom in situation calculus? a. Holding(g,s) Poss(Release(g),s) b.
More informationVision as Bayesian inference: analysis by synthesis?
Vision as Bayesian inference: analysis by synthesis? Schwarz Andreas, Wiesner Thomas 1 / 70 Outline Introduction Motivation Problem Description Bayesian Formulation Generative Models Letters, Text Faces
More informationLECTURE 5: REACTIVE AND HYBRID ARCHITECTURES
Reactive Architectures LECTURE 5: REACTIVE AND HYBRID ARCHITECTURES An Introduction to MultiAgent Systems http://www.csc.liv.ac.uk/~mjw/pubs/imas There are many unsolved (some would say insoluble) problems
More informationStepwise Knowledge Acquisition in a Fuzzy Knowledge Representation Framework
Stepwise Knowledge Acquisition in a Fuzzy Knowledge Representation Framework Thomas E. Rothenfluh 1, Karl Bögl 2, and Klaus-Peter Adlassnig 2 1 Department of Psychology University of Zurich, Zürichbergstraße
More informationJ-01 State intervention goals in observable and measureable terms.
BACB 4 th Edition Task List s Content Area J: Intervention J-01 State intervention goals in observable and measureable terms. J-02 Identify potential interventions based on assessment results and the best
More informationMETHODOLOGY FOR DISSERTATION
METHODOLOGY FOR DISSERTATION In order to expose the methods of scientific work, it is necessary to briefly clarify the terms of methodology, methods and scientific methods. The methodology comes from the
More informationConnectivity in a Bipolar Fuzzy Graph and its Complement
Annals of Pure and Applied Mathematics Vol. 15, No. 1, 2017, 89-95 ISSN: 2279-087X (P), 2279-0888(online) Published on 11 December 2017 www.researchmathsci.org DOI: http://dx.doi.org/10.22457/apam.v15n1a8
More informationComputational Tree Logic and Model Checking A simple introduction. F. Raimondi
Computational Tree Logic and Model Checking A simple introduction F. Raimondi I am Franco Raimondi, f.raimondi@cs.ucl.ac.uk Slides, coursework, coursework solutions can be found online: http://www.cs.ucl.ac.uk/staff/f.raimondi/
More informationCS343: Artificial Intelligence
CS343: Artificial Intelligence Introduction: Part 2 Prof. Scott Niekum University of Texas at Austin [Based on slides created by Dan Klein and Pieter Abbeel for CS188 Intro to AI at UC Berkeley. All materials
More informationARTIFICIAL INTELLIGENCE
ARTIFICIAL INTELLIGENCE LECTURE # 04 Artificial Intelligence 2012 Lecture 04 Delivered By Zahid Iqbal 1 Review of Last Lecture Artificial Intelligence 2012 Lecture 04 Delivered By Zahid Iqbal 2 Review
More informationA FRAMEWORK FOR CLINICAL DECISION SUPPORT IN INTERNAL MEDICINE A PRELIMINARY VIEW Kopecky D 1, Adlassnig K-P 1
A FRAMEWORK FOR CLINICAL DECISION SUPPORT IN INTERNAL MEDICINE A PRELIMINARY VIEW Kopecky D 1, Adlassnig K-P 1 Abstract MedFrame provides a medical institution with a set of software tools for developing
More informationSupervisory control synthesis
System view Engineering SCS Tools Applications Conclusions Supervisory control synthesis Bert van Beek Systems Engineering Group Dept. of Mechanical Engineering 5 November 2009 Bert van Beek, Industry
More informationSymbolic CTL Model Checking
Symbolic CTL Model Checking Crystal Chang Din Precise Modeling and Analysis group (PMA), University of Oslo INF9140 - Specification and Verification of Parallel Systems Crystal Chang Din @ UiO Symbolic
More informationIntro to Theory of Computation
Intro to Theory of Computation LECTURE 13 Last time: Turing Machines and Variants Today Turing Machine Variants Church-Turing Thesis Sofya Raskhodnikova 2/23/2016 Sofya Raskhodnikova; based on slides by
More informationAssignment Question Paper I. 1. What is the external and internal commands and write 5 commands for each with result?
Subject:- INFORMATION TECHNOLOGY Max Marks -30 kvksa esa gy djuk vfuok;z gsa 1. What is the external and internal commands and write 5 commands for each with result? 2. Writes the features of UNIX Operating
More informationAppendix I Teaching outcomes of the degree programme (art. 1.3)
Appendix I Teaching outcomes of the degree programme (art. 1.3) The Master graduate in Computing Science is fully acquainted with the basic terms and techniques used in Computing Science, and is familiar
More informationA Virtual Glucose Homeostasis Model for Verification, Simulation and Clinical Trials
A Virtual Glucose Homeostasis Model for Verification, Simulation and Clinical Trials Neeraj Kumar Singh INPT-ENSEEIHT/IRIT University of Toulouse, France September 14, 2016 Neeraj Kumar Singh A Perspective
More informationExperimentally Investigating the Effects of Defects in UML Models
Experimentally Investigating the Effects of Defects in UML Models Christian Lange C.F.J.Lange@TUE.nl www.win.tue.nl/~clange www.win.tue.nl/empanada SAN meeting February 11 th, 25 Overview Introduction
More informationICS 606. Intelligent Autonomous Agents 1. Intelligent Autonomous Agents ICS 606 / EE 606 Fall Reactive Architectures
Intelligent Autonomous Agents ICS 606 / EE 606 Fall 2011 Nancy E. Reed nreed@hawaii.edu 1 Lecture #5 Reactive and Hybrid Agents Reactive Architectures Brooks and behaviors The subsumption architecture
More informationThe Semantics of Intention Maintenance for Rational Agents
The Semantics of Intention Maintenance for Rational Agents Michael P. Georgeffand Anand S. Rao Australian Artificial Intelligence Institute Level 6, 171 La Trobe Street, Melbourne Victoria 3000, Australia
More informationINFERENCING STRATEGIES
INFERENCING STRATEGIES Table of Content Introduction Categories of Reasoning Inference Techniques Control Strategies Comparative Summary of Backward and Forward Chaining Conflict Resolution Goal Agenda
More informationPART - A 1. Define Artificial Intelligence formulated by Haugeland. The exciting new effort to make computers think machines with minds in the full and literal sense. 2. Define Artificial Intelligence
More informationRemarks on Bayesian Control Charts
Remarks on Bayesian Control Charts Amir Ahmadi-Javid * and Mohsen Ebadi Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran * Corresponding author; email address: ahmadi_javid@aut.ac.ir
More informationquadri: bumps in the road from language to data
quadri: bumps in the road from language to data presented by richard waldinger joint work with cleo condoravdi danny bobrow, kyle richardson, and amar das 9 march 2012 why do we need logic? Want to distinguish
More informationTWO HANDED SIGN LANGUAGE RECOGNITION SYSTEM USING IMAGE PROCESSING
134 TWO HANDED SIGN LANGUAGE RECOGNITION SYSTEM USING IMAGE PROCESSING H.F.S.M.Fonseka 1, J.T.Jonathan 2, P.Sabeshan 3 and M.B.Dissanayaka 4 1 Department of Electrical And Electronic Engineering, Faculty
More informationIII Semester M.Tech. (IT) Examination, Dec. 2010/Jan LOGIC AND FUNCTIONAL PROGRAMMING PART-A
MT 31 A III Semester M.Tech. (IT) Examination, Dec. 2010/Jan. 2011 LOGIC AND FUNCTIONAL PROGRAMMING 1. Distinguish between functional and conventional programming. 2. Define clause and definite program
More informationComprehensive Mitigation Framework for Concurrent Application of Multiple Clinical Practice Guidelines
Comprehensive Mitigation Framework for Concurrent Application of Multiple Clinical Practice Guidelines Szymon Wilk a,b,, Martin Michalowski c, Wojtek Michalowski b, Daniela Rosu b, Marc Carrier d, Mounira
More informationArtificial Intelligence and Human Thinking. Robert Kowalski Imperial College London
Artificial Intelligence and Human Thinking Robert Kowalski Imperial College London 1 Artificial Intelligence and Human Thinking The Abductive Logic Programming (ALP) agent model as a unifying framework
More informationParticular challenges of evaluation of herbal combination products
Particular challenges of evaluation of herbal combination products Reinhard Länger, PhD, Assoc. Prof. Austrian Medicines and Medical Devices Agency (AGES MEA/BASG) Dept. for Herbal, Homeopathic & Veterinary
More informationFormal Methods for Biological Systems: Languages, Algorithms, and Applications
Formal Methods for Biological Systems: Languages, Algorithms, and Applications Qinsi Wang CMU-CS-16-129 September 2016 School of Computer Science Computer Science Department Carnegie Mellon University
More informationVolker Halbach, Axiomatic Theories of Truth, Cambridge: Cambridge. University Press, 2011, ix+365pp., $85.00, ISBN
Volker Halbach, Axiomatic Theories of Truth, Cambridge: Cambridge University Press, 2011, ix+365pp., $85.00, ISBN 978-0-521-11581-0. The work under review provides the first book-length study wholly devoted
More informationCS 4365: Artificial Intelligence Recap. Vibhav Gogate
CS 4365: Artificial Intelligence Recap Vibhav Gogate Exam Topics Search BFS, DFS, UCS, A* (tree and graph) Completeness and Optimality Heuristics: admissibility and consistency CSPs Constraint graphs,
More informationDeepASL: Enabling Ubiquitous and Non-Intrusive Word and Sentence-Level Sign Language Translation
DeepASL: Enabling Ubiquitous and Non-Intrusive Word and Sentence-Level Sign Language Translation Biyi Fang Michigan State University ACM SenSys 17 Nov 6 th, 2017 Biyi Fang (MSU) Jillian Co (MSU) Mi Zhang
More information1. Stating the purpose of the speech is the first step in creating a speech.
2 From A to Z: Overview of a Speech True/False Questions 1. Stating the purpose of the speech is the first step in creating a speech. 2. Audience analysis involves making random guesses about how the audience
More informationMOLOGEN AG Jefferies 2014 London Healthcare Conference
Jefferies 2014 London Healthcare Conference Dr. Matthias Schroff Chief Executive Officer London 20 November 2014 V1-6 Disclaimer Certain statements in this presentation contain formulations or terms referring
More information39th SESSION OF THE SUBCOMMITTEE ON PLANNING AND PROGRAMMING OF THE EXECUTIVE COMMITTEE
PAN AMERICAN HEALTH ORGANIZATION WORLD HEALTH ORGANIZATION 39th SESSION OF THE SUBCOMMITTEE ON PLANNING AND PROGRAMMING OF THE EXECUTIVE COMMITTEE Washington, D.C., USA, 16-18 March 2005 Provisional Agenda
More informationDEVELOPMENT OF ELECTROMAGNETIC ACOUSTIC TRANSDUCER (EMAT) PHASED ARRAYS FOR SFR INSPECTION
DEVELOPMENT OF ELECTROMAGNETIC ACOUSTIC TRANSDUCER (EMAT) PHASED ARRAYS FOR SFR INSPECTION QNDE 2013 Florian LE BOURDAIS and Benoit MARCHAND CEA LIST, Centre de Saclay F-91191 Gif-sur-Yvette, France OUTLINE
More informationThe Current Status of Cardiac Electrophysiology in ESC Member Countries J. Brugada, P. Vardas, C. Wolpert
Albania. Algeria. Armenia. Austria. Belarus. Belgium. Bosnia & Herzegovina. Bulgaria. Croatia. Cyprus. Czech Republic Denmark. Egypt. Estonia. Finland. Former Yugoslav Republic of Macedonia. France. Georgia.
More informationA test bench to improve registration using RGB-D sensors
A test bench to improve registration using RGB-D sensors François Pomerleau and Stéphane Magnenat and Francis Colas and Ming Liu and Roland Siegwart April 8, 2011 - European Robotics Forum, Västerås, Sweeden
More informationUniversity of Bristol - Explore Bristol Research. Peer reviewed version. Link to publication record in Explore Bristol Research PDF-document
Araiza Illan, D., Pipe, A. G., & Eder, K. I. (2016). Intelligent Agent-Based Stimulation for Testing RoboticaSoftware in Human-Robot Interaction. arxiv, [1604.05508]. Peer reviewed version Link to publication
More informationTau: A Web-Deployed Hybrid Prover for First-Order Logic with Identity, with Optional Inductive Proof
Tau: A Web-Deployed Hybrid Prover for First-Order Logic with Identity, with Optional Inductive Proof Jay Halcomb, Randall R. Schulz H&S Information Systems http://hsinfosystems.com mailto:hsis@hsinfosystems.com
More informationWhy did the network make this prediction?
Why did the network make this prediction? Ankur Taly (Google Inc.) Joint work with Mukund Sundararajan and Qiqi Yan Some Deep Learning Successes Source: https://www.cbsinsights.com Deep Neural Networks
More informationLeman Akoglu Hanghang Tong Jilles Vreeken. Polo Chau Nikolaj Tatti Christos Faloutsos SIAM International Conference on Data Mining (SDM 2013)
Leman Akoglu Hanghang Tong Jilles Vreeken Polo Chau Nikolaj Tatti Christos Faloutsos 2013 SIAM International Conference on Data Mining (SDM 2013) What can we say about this list of authors? Use relational
More informationSemantic Structure of the Indian Sign Language
Semantic Structure of the Indian Sign Language Purushottam Kar and Achla M. Raina Indian Institute of Technology Kanpur 6 January 2008 Overview Indian Sign Language An Introduction Sociolinguistic and
More informationA Unified Approach to Ranking in Probabilistic Databases. Jian Li, Barna Saha, Amol Deshpande University of Maryland, College Park, USA
A Unified Approach to Ranking in Probabilistic Databases Jian Li, Barna Saha, Amol Deshpande University of Maryland, College Park, USA Probabilistic Databases Motivation: Increasing amounts of uncertain
More informationHuman Chromosomes. Lesson Overview. Lesson Overview Human Chromosomes
Lesson Overview 14.1 THINK ABOUT IT If you had to pick an ideal organism for the study of genetics, would you choose one that produced lots of offspring, was easy to grow in the lab, and had a short life
More informationProbabilistic Logic Networks in a Nutshell
Probabilistic Logic Networks in a Nutshell Matthew Ikle 1 1 Adams State College, Alamosa CO Abstract. We begin with a brief overview of Probabilistic Logic Networks, distinguish PLN from other approaches
More informationAlternative indicators for the risk of non-response bias
Alternative indicators for the risk of non-response bias Federal Committee on Statistical Methodology 2018 Research and Policy Conference Raphael Nishimura, Abt Associates James Wagner and Michael Elliott,
More informationChoice of Temporal Logic Specifications. Narayanan Sundaram EE219C Lecture
Choice of Temporal Logic Specifications Narayanan Sundaram EE219C Lecture 1 CTL Vs LTL The Final Showdown 2 Why should we choose one over the other? Expressiveness Clarity/Intuitiveness Algorithmic Complexity
More informationDecisions and Dependence in Influence Diagrams
JMLR: Workshop and Conference Proceedings vol 52, 462-473, 2016 PGM 2016 Decisions and Dependence in Influence Diagrams Ross D. hachter Department of Management cience and Engineering tanford University
More informationPlan Recognition through Goal Graph Analysis
Plan Recognition through Goal Graph Analysis Jun Hong 1 Abstract. We present a novel approach to plan recognition based on a two-stage paradigm of graph construction and analysis. First, a graph structure
More informationImplementation of Inference Engine in Adaptive Neuro Fuzzy Inference System to Predict and Control the Sugar Level in Diabetic Patient
, ISSN (Print) : 319-8613 Implementation of Inference Engine in Adaptive Neuro Fuzzy Inference System to Predict and Control the Sugar Level in Diabetic Patient M. Mayilvaganan # 1 R. Deepa * # Associate
More informationPractical Reference Dosimetry Course April 2015 PRDC Program, at a glance. Version 1.0. Day 1 Day 2 Day 3 Day 4
Practical Reference Dosimetry Course 21-24 April 2015 PRDC 2015 Program, at a glance Version 1.0 Day 1 Day 2 Day 3 Day 4 Quantities and Units Free air chambers Uncertainties Brachytherapy traceability
More informationSolving problems by searching
Solving problems by searching Chapter 3 14 Jan 2004 CS 3243 - Blind Search 1 Outline Problem-solving agents Problem types Problem formulation Example problems Basic search algorithms 14 Jan 2004 CS 3243
More informationTranslating From Perceptual to Cognitive Coding
Translating From Perceptual to Cognitive Coding Tyler Davis (thdavis@mail.utexas.edu) Bradley C. Love (brad_love@mail.utexas.edu) W. Todd Maddox (maddox@psy.utexas.edu) Department of Psychology, The University
More informationSteiner Tree Problems with Side Constraints Using Constraint Programming
Steiner Tree Problems with Side Constraints Using Constraint Programming AAAI 2016 Diego de Uña, Graeme Gange, Peter Schachte and Peter J. Stuckey February 14, 2016 University of Melbourne CIS Department
More informationIEEE SIGNAL PROCESSING LETTERS, VOL. 13, NO. 3, MARCH A Self-Structured Adaptive Decision Feedback Equalizer
SIGNAL PROCESSING LETTERS, VOL 13, NO 3, MARCH 2006 1 A Self-Structured Adaptive Decision Feedback Equalizer Yu Gong and Colin F N Cowan, Senior Member, Abstract In a decision feedback equalizer (DFE),
More informationOverview. cis32-spring2003-parsons-lect15 2
EXPERT SYSTEMS Overview The last lecture looked at rules as a technique for knowledge representation. In a while we will go on to look at logic as a means of knowledge representation. First, however, we
More informationA Unified View of Consequence Relation, Belief Revision and Conditional Logic
A Unified View of Consequence Relation, Belief Revision and Conditional Logic Hirofumi Katsuno NTT Basic Research Laboratories Musashinoshi, Tokyo 180 Japan katsuno(dntt-20.ntt. jp Ken Satoh ICOT Research
More informationOverview EXPERT SYSTEMS. What is an expert system?
EXPERT SYSTEMS Overview The last lecture looked at rules as a technique for knowledge representation. In a while we will go on to look at logic as a means of knowledge representation. First, however, we
More informationArtificial Intelligence Lecture 7
Artificial Intelligence Lecture 7 Lecture plan AI in general (ch. 1) Search based AI (ch. 4) search, games, planning, optimization Agents (ch. 8) applied AI techniques in robots, software agents,... Knowledge
More informationMinimal Change and Maximal Coherence: A Basis for Belief Revision and Reasoning about Actions
Minimal Change and Maximal Coherence: A Basis for Belief Revision and Reasoning about Actions Anand S. Rao Australian AI Institute Carlton, Vic-3053 Australia Email: anand@aaii.oz.au Abstract The study
More informationEfficient AUC Optimization for Information Ranking Applications
Efficient AUC Optimization for Information Ranking Applications Sean J. Welleck IBM, USA swelleck@us.ibm.com Abstract. Adequate evaluation of an information retrieval system to estimate future performance
More informationProject PRACE 1IP, WP7.4
Project PRACE 1IP, WP7.4 Plamenka Borovska, Veska Gancheva Computer Systems Department Technical University of Sofia The Team is consists of 5 members: 2 Professors; 1 Assist. Professor; 2 Researchers;
More informationWhat can Ontology Contribute to an Interdisciplinary Project?
What can Ontology Contribute to an Interdisciplinary Project? Andrew U. Frank Geoinformation TU Vienna frank@geoinfo.tuwien.ac.at www.geoinfo.tuwien.ac.at Andrew Frank 1 My naïve view of archeology: How
More informationGoal Recognition through Goal Graph Analysis
Journal of Artificial Intelligence Research 15 (2001) 1-30 Submitted 11/00; published 7/01 Goal Recognition through Goal Graph Analysis Jun Hong School of Information and Software Engineering University
More informationTHE IMPORTANCE OF MENTAL OPERATIONS IN FORMING NOTIONS
THE IMPORTANCE OF MENTAL OPERATIONS IN FORMING NOTIONS MARIA CONDOR MONICA CHIRA monica.chira@orange_ftgroup.com Abstract: In almost every moment of our existence we are engaged in a process of problem
More informationPythia WEB ENABLED TIMED INFLUENCE NET MODELING TOOL SAL. Lee W. Wagenhals Alexander H. Levis
Pythia WEB ENABLED TIMED INFLUENCE NET MODELING TOOL Lee W. Wagenhals Alexander H. Levis ,@gmu.edu Adversary Behavioral Modeling Maxwell AFB, Montgomery AL March 8-9, 2007 1 Outline Pythia
More informationDopamine and Reward Prediction Error
Dopamine and Reward Prediction Error Professor Andrew Caplin, NYU The Center for Emotional Economics, University of Mainz June 28-29, 2010 The Proposed Axiomatic Method Work joint with Mark Dean, Paul
More informationCHAPTER - 6 STATISTICAL ANALYSIS. This chapter discusses inferential statistics, which use sample data to
CHAPTER - 6 STATISTICAL ANALYSIS 6.1 Introduction This chapter discusses inferential statistics, which use sample data to make decisions or inferences about population. Populations are group of interest
More informationSTATISTICAL METHODS FOR DIAGNOSTIC TESTING: AN ILLUSTRATION USING A NEW METHOD FOR CANCER DETECTION XIN SUN. PhD, Kansas State University, 2012
STATISTICAL METHODS FOR DIAGNOSTIC TESTING: AN ILLUSTRATION USING A NEW METHOD FOR CANCER DETECTION by XIN SUN PhD, Kansas State University, 2012 A THESIS Submitted in partial fulfillment of the requirements
More informationModeling Crowd Behavior Based on Social Comparison Theory: Extended Abstract
Modeling Crowd Behavior Based on Social Comparison Theory: Extended Abstract Natalie Fridman and Gal Kaminka Bar Ilan University, Israel The MAVERICK Group Computer Science Department {fridman,galk}cs.biu.ac.il
More informationInferencing in Artificial Intelligence and Computational Linguistics
Inferencing in Artificial Intelligence and Computational Linguistics (http://www.dfki.de/~horacek/infer-ai-cl.html) no classes on 28.5., 18.6., 25.6. 2-3 extra lectures will be scheduled Helmut Horacek
More informationIncorporating Action into Diagnostic Problem Solving (An Abridged Report)
Incorporating Action into Diagnostic Problem Solving (An Abridged Report) Sheila A. McIlraith Department of Computer Science, University of Toronto Toronto, ON M5S 1A4 Canada e-mail: mcilrait@cs.toronto.edu
More informationDynamic Stochastic Synapses as Computational Units
Dynamic Stochastic Synapses as Computational Units Wolfgang Maass Institute for Theoretical Computer Science Technische Universitat Graz A-B01O Graz Austria. email: maass@igi.tu-graz.ac.at Anthony M. Zador
More informationmobile monitoring... the fastest way to improved health
mobile monitoring... the fastest way to improved health 1 topics } telozo the company behind the clue series } general information - clue series } clue - overview } clue medical - overview } modes of data
More informationUse of GELLO v.1.x, GLIF 3.5, SNOMED CT and EN archetypes
Use of GELLO v.1.x, GLIF 3.5, SNOMED CT and EN 13606 archetypes Peter Scott Andrew McIntyre Jared Davision Peter Tattam www.medical objects.com.au peter@medical objects.com.au Some main points 1. Standards
More informationNODE CONNECTIVITY AND CONDITIONAL DENSITY D. White and F. Harary. Definitions of Cohesive Sets in Social Networks
NODE CONNECTIVITY AND CONDITIONAL DENSITY D. White and F. Harary Definitions of Cohesive Sets in Social Networks The connectivity K (G) kappa of a connected graph G is the minimum number of nodes needed
More informationClassification and Predication of Breast Cancer Risk Factors Using Id3
The International Journal Of Engineering And Science (IJES) Volume 5 Issue 11 Pages PP 29-33 2016 ISSN (e): 2319 1813 ISSN (p): 2319 1805 Classification and Predication of Breast Cancer Risk Factors Using
More information62nd Session of the IAEA General Conference. 17 to 21 September Statement by
Unofficial translation 62nd Session of the IAEA General Conference 17 to 21 September 2018 Statement by Mr Benoît Revaz State Secretary and Director of the Swiss Federal Office of Energy Vienna, 18 September
More informationThe elements of cancer and palliative care reform in Victoria
The elements of cancer and palliative care reform in Victoria Dr Chris Brook Executive Director Rural and Regional Health and Aged Care Services Department of Human Services 1 Overview Rural and regional
More informationGregor Mendel father of heredity
MENDEL AND MEIOSIS Gregor Mendel father of heredity MENDEL S LAWS OF HEREDITY Heredity branch of genetics dealing with the passing on of traits from parents to offspring Pea Plants Easy maintenance & large
More informationSelecting a research method
Selecting a research method Tomi Männistö 13.10.2005 Overview Theme Maturity of research (on a particular topic) and its reflection on appropriate method Validity level of research evidence Part I Story
More informationLecture 2: Foundations of Concept Learning
Lecture 2: Foundations of Concept Learning Cognitive Systems - Machine Learning Part I: Basic Approaches to Concept Learning Version Space, Candidate Elimination, Inductive Bias last change October 18,
More informationDEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING. CP 7026-Software Quality Assurance Unit-I. Part-A
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING CP 7026-Software Quality Assurance Unit-I 1. What is quality? 2. What are the Views of Quality? 3. What is Quality Cost? 4. What is Quality Audit? 5. What
More informationLearning and Adaptive Behavior, Part II
Learning and Adaptive Behavior, Part II April 12, 2007 The man who sets out to carry a cat by its tail learns something that will always be useful and which will never grow dim or doubtful. -- Mark Twain
More informationThe Temporal Lobes Language and beyond. 8 th Symposium on Behavioral Neurology Lucerne, 10 May Neurocenter. Kompetenz, die lächelt.
Spitalregion Luzern/Nidwalden Neurocenter The Temporal Lobes Language and beyond 8 th Symposium on Behavioral Neurology Lucerne, 10 May 2019 Kompetenz, die lächelt. Editorial Dear colleagues, it is our
More informationClusters, Symbols and Cortical Topography
Clusters, Symbols and Cortical Topography Lee Newman Thad Polk Dept. of Psychology Dept. Electrical Engineering & Computer Science University of Michigan 26th Soar Workshop May 26, 2006 Ann Arbor, MI agenda
More informationABSTRACT OF THE DOCTORAL THESIS BY MS. ANASTASIA TSOUROUFLI
ABSTRACT OF THE DOCTORAL THESIS BY MS. ANASTASIA TSOUROUFLI Thesis submitted to: NATIONAL ACADEMY OF PHYSICAL EDUCATION AND SPORTS Bucharest, Romania, 2009 Thesis Advisor: Thesis Title: Prof. Dr. ADRIAN
More informationComputation Tree Logic vs. Linear Temporal Logic. Slides from Robert Bellarmine Krug University of Texas at Austin
Computation Tree Logic vs. Linear Temporal Logic Slides from Robert Bellarmine Krug University of Texas at Austin CTL vs. LTL (2 / 40) Outline 1. Some Definitions And Notation 2. LTL 3. CTL 4. CTL vs.
More informationA Computational Theory of Belief Introspection
A Computational Theory of Belief Introspection Kurt Konolige Artificial Intelligence Center SRI International Menlo Park, California 94025 Abstract Introspection is a general term covering the ability
More informationModel-Based Reasoning Methods within an Ambient Intelligent Agent Model
Model-Based Reasoning Methods within an Ambient Intelligent Agent Model Tibor Bosse, Fiemke Both, Charlotte Gerritsen, Mark Hoogendoorn, and Jan Treur Vrije Universiteit Amsterdam, Department of Artificial
More informationHHS Public Access Author manuscript Hum Mutat. Author manuscript; available in PMC 2016 April 16.
GeneMatcher: A Matching Tool for Connecting Investigators with an Interest in the Same Gene Nara Sobreira 1,*, François Schiettecatte 2, David Valle 1, and Ada Hamosh 1 1 Institute of Genetic Medicine,
More informationUsing Phylogenetic Structure to Assess the Evolutionary Ecology of Microbiota! TJS! iseem Call! April 2015!
Using Phylogenetic Structure to Assess the Evolutionary Ecology of Microbiota! TJS! iseem Call! April 2015! How are Microbes Distributed In Nature?! A major question in microbial ecology! Used to assess
More informationThe UML/MARTE Verifier
ETR course The UML/MARTE Verifier A Property Driven toolchain for model checking real time systems Marc Pantel (based on Ning Ge and Faiez Zalila work) Université de Toulouse, IRIT-CNRS, ACADIE August
More informationFoundations of AI. 10. Knowledge Representation: Modeling with Logic. Concepts, Actions, Time, & All the Rest
Foundations of AI 10. Knowledge Representation: Modeling with Logic Concepts, Actions, Time, & All the Rest Wolfram Burgard, Andreas Karwath, Bernhard Nebel, and Martin Riedmiller 10/1 Contents Knowledge
More informationLEAF Marque Assurance Programme
Invisible ISEAL Code It is important that the integrity of the LEAF Marque Standard is upheld therefore the LEAF Marque Standards System has an Assurance Programme to ensure this. This document outlines
More information