Interactive Visualizations for Cyber-
|
|
- Cynthia Allen
- 5 years ago
- Views:
Transcription
1 Interactive Visualizations for Cyber- Mission Awareness ARO MURI on Cyber Situation Awareness Year One Review Meeting Tobias Höllerer Four Eyes Laboratory (Imaging, Interaction, and Innovative Interfaces), Computer Science Department, Media Arts & Technology Program,
2 Motivation 1. Up-to-date views of the available cyber-assets 2. A comprehensive analysis of the dependencies between cyber-missions and cyber-assets, 3. An accurate understanding of the impact of cyberattacks 4. Actionable cyber-attack forecasts 5. A semantically-rich, easy-to-grasp view of the cyber- mission i status. t
3 Approach Scalable Visualization and Interaction Effective information and knowledge presentation by tailoring interfaces to user s information needs, context, and cognitive state. User models (e.g. war fighters, network security officers, command center personnel) Display and interaction platforms (mobile interfaces, desktop, immersive situation rooms) Our integrative framework and the data structures we share (from data modeling and acquisition, extraction and abstraction, and analysis and presentation) enables such dynamic tailoring. Enable users to interactively explore the information landscape. 3
4 Approach Scalable Visualization and Interaction Effective information and knowledge presentation by tailoring interfaces to user s information needs, context, and cognitive state. User models (e.g. war fighters, network security officers, command center personnel) Display and interaction platforms (mobile interfaces, desktop, immersive situation rooms) Our integrative framework and the data structures we share (from data modeling and acquisition, extraction and abstraction, and analysis and presentation) enables such dynamic tailoring. Enable users to interactively explore the information landscape. 4
5 Access To Data Lawrence Berkeley National Lab (LBL) logs ~4,000 users, ~12,000 internal hosts, Gbps/10Gbps Ground truth th (or at least partial) available Topology, historical DNS also available UCSB network logs and trouble tickets Set up network logging facilities with Engineering Computing Infrastructure at UCSB Netflow from switches in 3 main engineering buildings Correlation with CS support trouble tickets
6 User/Task Analysis Main user types: Network security officers at different levels Command center, mission planning Network security officers Most likely standard desktop computer and display, but might switch to mobile interface in extraordinary situations. Cybaware visualizations need to be easily shared / networked Officers need to maintain overview of mission timeline including assets and their use, as well as all incoming information, potential threats, their impact, and possible countermeasures. Mission planners and some officers may work in the situation room, where we assume high-end display and interaction hardware to be available. 6
7 User/Task Analysis Main user types: Network security officers at different levels Command center, mission planning Network security officers Most likely standard desktop computer and display, but might switch to mobile interface in extraordinary situations. Cybaware visualizations need to be easily shared / networked Officers need to maintain overview of mission timeline including assets and their use, as well as all incoming information, potential threats, their impact, and possible countermeasures. Mission planners and some officers may work in the situation room, where we assume high-end display and interaction hardware to be available. 7
8 Platform Evaluation Mobile Platform Desktop / Networked Collaboration Immersive Situation ti Room 8
9 Ebb context-aware timelines
10 Platform Evaluation Mobile Platform Desktop / Networked Collaboration Immersive Situation ti Room 10
11 Platform Evaluation Mobile Platform Desktop / Networked Collaboration Immersive Situation ti Room UCSB Allosphere 11
12 Desktop / Networked Collaboration Networked Graph Views WIGIs: Web-based Interactive Graph Interfaces Demo 12
13 Cybaware NSR NSR (Network Simulation Realm) is an immersive 3D visualization tool for cybersecurity situational awareness. Network Topologies Datasets Framework for defining and importing network topologies, registering relevant data sets, and rendering a space containing these entities in a situation room or on a desktop PC. Space Desktop PC / AlloSphere
14 Cybaware NSR Network Topologies Plug-ins Space Datasets A key element of our framework is a plug-in based architecture allowing users to build and deploy any number of custom visualization agents into the space. These plug-in agents can annotate and augment the network entities in the space in order to provide real-time analysis, feedback, or suggestions to the user. Desktop PC / AlloSphere
15 Plugin Example: Visualizing Game Theoretic Problems Goal: Visualize information about game theoretic problems to aid the decision making process Will enable interactive what-if analyses of attack scenarios Here, refer to a game as a set of moves (game can be incomplete) First step: Visualize data from game trees
16 Game Trees Example: Tic Tac Toe game tree Root: empty game X s turn, 9 possible x moves, 9 children per game (if game isn t over) O s turn, 8 possible moves, 8 children per game (if game isn t over) x o o x x x x... o o o x x x x o o o x o x o x 2 nd level of tree: game with 1 move Leaves: games that are done (win/lose/draw) X wins O wins Draw
17 Visualizing Game Trees via Treemaps Treemap: Area-efficient Areaefficient representation of tree, usually on a 2D surface Game Tree: Nodes correspond to games, leaves are completed games Corresponding treemap construction Initial region for root node... X0 X1 O1 O2 O8... X8 Divide each region horizontally for each game resulting from X s next move Divide region vertically for each game resulting from O s next move Further divisions X wins (Blue) O Wins (Orange) Draw (Black) Final look of entire treemap after coloring Once at region corresponding to moves corresponding to completed game (tree leaf), color region and stop dividing
18 Resulting Treemap Treemap for complete (9 moves max) Tic Tac Toe game tree (~250k leaves): Tic Tac Toe game treemap Tic Tac Toe game treemap with regions corresponding X s Xs first move highlighted (X can play moves 0 8 at this point In the game) We are interested in subregions of the treemap that correspond to available moves. This allows us to see the possible set of outcomes corresponding to a certain move.
19 GameTreemap plugin/app (OpenGL) Allows user to test Tic-Tac-Toe Tac Toe moves with treemap visualization Game board and data Treemaps Treemap for entire game with region corresponding to current set of moves highlighted Region of treemap corresponding to current game with current player s available moves highlighted. Current selected move is highlighted with thicker line. Region of treemap corresponding to game if current player selects current selected move. Subregions with next player s possible moves highlighted; helps identify imminent threats.
20 Demo in AlloSphere 3:30pm today 20
21 Conclusions Scalable Information Presentation Networked Graphs and Information Browsing Mission Control in Immersive Situation Rooms Preparatory Work on Mobile Platforms Interfaces will Scale with Data UCSB network logs and trouble tickets Lawrence Berkeley National Lab (LBL) logs Support for Interactive Situational Awareness Resources Overview Adversary Alertness What-If Scenarios 21
22 22
Mission-focused Interaction and Visualization for Cyber-Awareness!
Mission-focused Interaction and Visualization for Cyber-Awareness! ARO MURI on Cyber Situation Awareness Year Two Review Meeting Tobias Höllerer Four Eyes Laboratory (Imaging, Interaction, and Innovative
More informationARTIFICIAL INTELLIGENCE (CS 370D)
Princess Nora University Faculty of Computer & Information Systems ARTIFICIAL INTELLIGENCE (CS 370D) (CHAPTER-5) ADVERSARIAL SEARCH ADVERSARIAL SEARCH Optimal decisions Min algorithm α-β pruning Imperfect,
More informationmywbut.com Two agent games : alpha beta pruning
Two agent games : alpha beta pruning 1 3.5 Alpha-Beta Pruning ALPHA-BETA pruning is a method that reduces the number of nodes explored in Minimax strategy. It reduces the time required for the search and
More informationCSC 110 Lab 4 Algorithms using Functions. Names:
CSC 110 Lab 4 Algorithms using Functions Names: Tic- Tac- Toe Game Write a program that will allow two players to play Tic- Tac- Toe. You will be given some code as a starting point. Fill in the parts
More informationGame-playing AIs: Games and Adversarial Search I AIMA
Game-playing AIs: Games and Adversarial Search I AIMA 5.1-5.2 Games: Outline of Unit Part I: Games as Search Motivation Game-playing AI successes Game Trees Evaluation Functions Part II: Adversarial Search
More informationModule 3. Problem Solving using Search- (Two agent) Version 2 CSE IIT, Kharagpur
Module 3 Problem Solving using Search- (Two agent) 3.1 Instructional Objective The students should understand the formulation of multi-agent search and in detail two-agent search. Students should b familiar
More informationAdversarial Search. Robert Platt Northeastern University. Some images and slides are used from: 1. CS188 UC Berkeley 2. RN, AIMA
Adversarial Search Robert Platt Northeastern University Some images and slides are used from: 1. CS188 UC Berkeley 2. RN, AIMA What is adversarial search? Adversarial search: planning used to play a game
More information2359 (i.e. 11:59:00 pm) on 4/16/18 via Blackboard
CS 109: Introduction to Computer Science Goodney Spring 2018 Homework Assignment 4 Assigned: 4/2/18 via Blackboard Due: 2359 (i.e. 11:59:00 pm) on 4/16/18 via Blackboard Notes: a. This is the fourth homework
More informationShared Imagination: Creative Collaboration in Mixed Reality. Charles Hughes Christopher Stapleton July 26, 2005
Shared Imagination: Creative Collaboration in Mixed Reality Charles Hughes Christopher Stapleton July 26, 2005 Examples Team performance training Emergency planning Collaborative design Experience modeling
More informationSven Wachsmuth Bielefeld University
& CITEC Central Lab Facilities Performance Assessment and System Design in Human Robot Interaction Sven Wachsmuth Bielefeld University May, 2011 & CITEC Central Lab Facilities What are the Flops of cognitive
More informationCS 2710 Foundations of AI. Lecture 9. Adversarial search. CS 2710 Foundations of AI. Game search
CS 2710 Foundations of AI Lecture 9 Adversarial search Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2710 Foundations of AI Game search Game-playing programs developed by AI researchers since
More informationCMPUT 396 Tic-Tac-Toe Game
CMPUT 396 Tic-Tac-Toe Game Recall minimax: - For a game tree, we find the root minimax from leaf values - With minimax we can always determine the score and can use a bottom-up approach Why use minimax?
More informationCS 331: Artificial Intelligence Adversarial Search II. Outline
CS 331: Artificial Intelligence Adversarial Search II 1 Outline 1. Evaluation Functions 2. State-of-the-art game playing programs 3. 2 player zero-sum finite stochastic games of perfect information 2 1
More informationArtificial Intelligence
Artificial Intelligence CS482, CS682, MW 1 2:15, SEM 201, MS 227 Prerequisites: 302, 365 Instructor: Sushil Louis, sushil@cse.unr.edu, http://www.cse.unr.edu/~sushil Games and game trees Multi-agent systems
More informationFuture of Cities. Harvard GSD. Smart[er] Citizens Bergamo University
Future of Cities Harvard GSD Smart[er] Citizens Bergamo University Future of Cities Harvard GSD Smart[er] Citizens Bergamo University SMART[ER] CITIES Harvard Graduate School of Design SCI 0637100 Spring
More informationSet 4: Game-Playing. ICS 271 Fall 2017 Kalev Kask
Set 4: Game-Playing ICS 271 Fall 2017 Kalev Kask Overview Computer programs that play 2-player games game-playing as search with the complication of an opponent General principles of game-playing and search
More informationThe LVCx Framework. The LVCx Framework An Advanced Framework for Live, Virtual and Constructive Experimentation
An Advanced Framework for Live, Virtual and Constructive Experimentation An Advanced Framework for Live, Virtual and Constructive Experimentation The CSIR has a proud track record spanning more than ten
More informationCS 1571 Introduction to AI Lecture 12. Adversarial search. CS 1571 Intro to AI. Announcements
CS 171 Introduction to AI Lecture 1 Adversarial search Milos Hauskrecht milos@cs.pitt.edu 39 Sennott Square Announcements Homework assignment is out Programming and experiments Simulated annealing + Genetic
More informationTic-tac-toe. Lars-Henrik Eriksson. Functional Programming 1. Original presentation by Tjark Weber. Lars-Henrik Eriksson (UU) Tic-tac-toe 1 / 23
Lars-Henrik Eriksson Functional Programming 1 Original presentation by Tjark Weber Lars-Henrik Eriksson (UU) Tic-tac-toe 1 / 23 Take-Home Exam Take-Home Exam Lars-Henrik Eriksson (UU) Tic-tac-toe 2 / 23
More informationNVIDIA APEX: From Mirror s Edge to Pervasive Cinematic Destruction. Anders Caspersson, DICE Monier Maher, NVIDIA Jean Pierre Bordes, NVIDIA
NVIDIA APEX: From Mirror s Edge to Pervasive Cinematic Destruction Anders Caspersson, DICE Monier Maher, NVIDIA Jean Pierre Bordes, NVIDIA Agenda Mirror s Edge Case study (Anders Caspersson) PhysX in Mirror
More informationMOBILIZE AND MAXIMIZE THE POTENTIAL OF P25 DIGITAL LMR
WHITE PAPER MOBILIZE AND MAXIMIZE THE POTENTIAL OF P25 DIGITAL LMR PAGE 1 ARE YOU LEVERAGING THE POTENTIAL OF YOUR P25 LMR NETWORK? Your customers expect you to be ready, capable and equipped for power
More informationTIES: An Engineering Design Methodology and System
From: IAAI-90 Proceedings. Copyright 1990, AAAI (www.aaai.org). All rights reserved. TIES: An Engineering Design Methodology and System Lakshmi S. Vora, Robert E. Veres, Philip C. Jackson, and Philip Klahr
More informationUbiquitous Home Simulation Using Augmented Reality
Proceedings of the 2007 WSEAS International Conference on Computer Engineering and Applications, Gold Coast, Australia, January 17-19, 2007 112 Ubiquitous Home Simulation Using Augmented Reality JAE YEOL
More informationArchitecting Systems of the Future, page 1
Architecting Systems of the Future featuring Eric Werner interviewed by Suzanne Miller ---------------------------------------------------------------------------------------------Suzanne Miller: Welcome
More informationA CYBER PHYSICAL SYSTEMS APPROACH FOR ROBOTIC SYSTEMS DESIGN
Proceedings of the Annual Symposium of the Institute of Solid Mechanics and Session of the Commission of Acoustics, SISOM 2015 Bucharest 21-22 May A CYBER PHYSICAL SYSTEMS APPROACH FOR ROBOTIC SYSTEMS
More informationCS 4700: Foundations of Artificial Intelligence
CS 4700: Foundations of Artificial Intelligence selman@cs.cornell.edu Module: Adversarial Search R&N: Chapter 5 1 Outline Adversarial Search Optimal decisions Minimax α-β pruning Case study: Deep Blue
More informationAnnouncements. Homework 1 solutions posted. Test in 2 weeks (27 th ) -Covers up to and including HW2 (informed search)
Minimax (Ch. 5-5.3) Announcements Homework 1 solutions posted Test in 2 weeks (27 th ) -Covers up to and including HW2 (informed search) Single-agent So far we have look at how a single agent can search
More informationExploring Virtual Reality (VR) with ArcGIS. Euan Cameron Simon Haegler Mark Baird
Exploring Virtual Reality (VR) with ArcGIS Euan Cameron Simon Haegler Mark Baird Agenda Introduction & Terminology Application & Market Potential Mobile VR with ArcGIS 360VR Desktop VR with CityEngine
More informationGenerating Virtual Environments by Linking Spatial Data Processing with a Gaming Engine
Generating Virtual Environments by Linking Spatial Data Processing with a Gaming Engine Christian STOCK, Ian D. BISHOP, and Alice O CONNOR 1 Introduction As the general public gets increasingly involved
More informationProject Example: wissen.de
Project Example: wissen.de Software Architecture VO/KU (707.023/707.024) Roman Kern KMI, TU Graz January 24, 2014 Roman Kern (KMI, TU Graz) Project Example: wissen.de January 24, 2014 1 / 59 Outline 1
More informationAdversarial Search. Rob Platt Northeastern University. Some images and slides are used from: AIMA CS188 UC Berkeley
Adversarial Search Rob Platt Northeastern University Some images and slides are used from: AIMA CS188 UC Berkeley What is adversarial search? Adversarial search: planning used to play a game such as chess
More informationAn Agent-based Heterogeneous UAV Simulator Design
An Agent-based Heterogeneous UAV Simulator Design MARTIN LUNDELL 1, JINGPENG TANG 1, THADDEUS HOGAN 1, KENDALL NYGARD 2 1 Math, Science and Technology University of Minnesota Crookston Crookston, MN56716
More informationMSc(CompSc) List of courses offered in
Office of the MSc Programme in Computer Science Department of Computer Science The University of Hong Kong Pokfulam Road, Hong Kong. Tel: (+852) 3917 1828 Fax: (+852) 2547 4442 Email: msccs@cs.hku.hk (The
More informationRules of the game. chess checkers tic-tac-toe...
Course 9 Games Rules of the game Two players: MAX and MIN Both have as goal to win the game Only one can win or else it will be a draw In the initial modeling there is no chance (but it can be simulated)
More informationMOBILIZE REALTIME INFORMATION SEAMLESSLY ACROSS YOUR OPERATION CONNECT PERSONNEL MORE SAFELY AND EFFICIENTLY WITH OUR MISSION CRITICAL SOLUTIONS
MOBILIZE REALTIME INFORMATION SEAMLESSLY ACROSS YOUR OPERATION CONNECT PERSONNEL MORE SAFELY AND EFFICIENTLY WITH OUR MISSION CRITICAL SOLUTIONS PROTECT WORKERS IN HAZARDOUS ENVIRONMENTS Oil and gas companies
More informationPlaying Games. Henry Z. Lo. June 23, We consider writing AI to play games with the following properties:
Playing Games Henry Z. Lo June 23, 2014 1 Games We consider writing AI to play games with the following properties: Two players. Determinism: no chance is involved; game state based purely on decisions
More informationAutonomous Robotic (Cyber) Weapons?
Autonomous Robotic (Cyber) Weapons? Giovanni Sartor EUI - European University Institute of Florence CIRSFID - Faculty of law, University of Bologna Rome, November 24, 2013 G. Sartor (EUI-CIRSFID) Autonomous
More information1 Introduction. 1.1 Game play. CSC 261 Lab 4: Adversarial Search Fall Assigned: Tuesday 24 September 2013
CSC 261 Lab 4: Adversarial Search Fall 2013 Assigned: Tuesday 24 September 2013 Due: Monday 30 September 2011, 11:59 p.m. Objectives: Understand adversarial search implementations Explore performance implications
More informationReal-time Cooperative Behavior for Tactical Mobile Robot Teams. September 10, 1998 Ronald C. Arkin and Thomas R. Collins Georgia Tech
Real-time Cooperative Behavior for Tactical Mobile Robot Teams September 10, 1998 Ronald C. Arkin and Thomas R. Collins Georgia Tech Objectives Build upon previous work with multiagent robotic behaviors
More informationDistributed Robotics: Building an environment for digital cooperation. Artificial Intelligence series
Distributed Robotics: Building an environment for digital cooperation Artificial Intelligence series Distributed Robotics March 2018 02 From programmable machines to intelligent agents Robots, from the
More informationExpression Of Interest
Expression Of Interest Modelling Complex Warfighting Strategic Research Investment Joint & Operations Analysis Division, DST Points of Contact: Management and Administration: Annette McLeod and Ansonne
More informationUnit 12: Artificial Intelligence CS 101, Fall 2018
Unit 12: Artificial Intelligence CS 101, Fall 2018 Learning Objectives After completing this unit, you should be able to: Explain the difference between procedural and declarative knowledge. Describe the
More informationArtificial Intelligence Lecture 3
Artificial Intelligence Lecture 3 The problem Depth first Not optimal Uses O(n) space Optimal Uses O(B n ) space Can we combine the advantages of both approaches? 2 Iterative deepening (IDA) Let M be a
More informationMission Space. Value-based use of augmented reality in support of critical contextual environments
Mission Space Value-based use of augmented reality in support of critical contextual environments Vicki A. Barbur Ph.D. Senior Vice President and Chief Technical Officer Concurrent Technologies Corporation
More informationGame Tree Search 1/6/17
Game Tree Search /6/7 Frameworks for Decision-Making. Goal-directed planning Agents want to accomplish some goal. The agent will use search to devise a plan.. Utility maximization Agents ascribe a utility
More informationRules of the game. chess checkers tic-tac-toe...
Course 8 Games Rules of the game Two players: MAX and MIN Both have as goal to win the game Only one can win or else it will be a draw In the initial modeling there is no chance (but it can be simulated)
More information2 person perfect information
Why Study Games? Games offer: Intellectual Engagement Abstraction Representability Performance Measure Not all games are suitable for AI research. We will restrict ourselves to 2 person perfect information
More informationTECHNOLOGY COMMONALITY FOR SIMULATION TRAINING OF AIR COMBAT OFFICERS AND NAVAL HELICOPTER CONTROL OFFICERS
TECHNOLOGY COMMONALITY FOR SIMULATION TRAINING OF AIR COMBAT OFFICERS AND NAVAL HELICOPTER CONTROL OFFICERS Peter Freed Managing Director, Cirrus Real Time Processing Systems Pty Ltd ( Cirrus ). Email:
More informationIndustry 4.0: the new challenge for the Italian textile machinery industry
Industry 4.0: the new challenge for the Italian textile machinery industry Executive Summary June 2017 by Contacts: Economics & Press Office Ph: +39 02 4693611 email: economics-press@acimit.it ACIMIT has
More informationAutonomous Control for Unmanned
Autonomous Control for Unmanned Surface Vehicles December 8, 2016 Carl Conti, CAPT, USN (Ret) Spatial Integrated Systems, Inc. SIS Corporate Profile Small Business founded in 1997, focusing on Research,
More informationCombining complementary skills, research, novel technologies.
The Company Farextra is a Horizon 2020 project spinoff at the forefront of a new industrial revolution. Focusing on AR and VR solutions in industrial training, safety and maintenance Founded on January
More informationDevelopment of CBRN Impact Assessment Capabilities
Development of CBRN Impact Assessment Capabilities Christopher Clem Defence Science and Technology Laboratory, UK Crown Copyright 2007. Published with the permission of the Defence Science and Technology
More informationSolving a Brew Mystery: Digital Forensics With The Dragos Platform and OSIsoft PI System
Solving a Brew Mystery: Digital Forensics With The Dragos Platform and OSIsoft PI System This paper presents a modern challenge of defending an industrial system, using situational awareness to detect
More informationEdward Waller Joseph Chaput Presented at the IAEA International Conference on Physical Protection of Nuclear Material and Facilities
Training and Exercising the Nuclear Safety and Nuclear Security Interface Incident Response through Synthetic Environment, Augmented Reality and Virtual Reality Simulations Edward Waller Joseph Chaput
More informationAr#ficial)Intelligence!!
Introduc*on! Ar#ficial)Intelligence!! Roman Barták Department of Theoretical Computer Science and Mathematical Logic So far we assumed a single-agent environment, but what if there are more agents and
More informationActivity-Centric Configuration Work in Nomadic Computing
Activity-Centric Configuration Work in Nomadic Computing Steven Houben The Pervasive Interaction Technology Lab IT University of Copenhagen shou@itu.dk Jakob E. Bardram The Pervasive Interaction Technology
More informationGame-Playing & Adversarial Search
Game-Playing & Adversarial Search This lecture topic: Game-Playing & Adversarial Search (two lectures) Chapter 5.1-5.5 Next lecture topic: Constraint Satisfaction Problems (two lectures) Chapter 6.1-6.4,
More informationNVIDIA APEX: High-Definition Physics with Clothing and Vegetation. Michael Sechrest, IDV Monier Maher, NVIDIA Jean Pierre Bordes, NVIDIA
NVIDIA APEX: High-Definition Physics with Clothing and Vegetation Michael Sechrest, IDV Monier Maher, NVIDIA Jean Pierre Bordes, NVIDIA Outline Introduction APEX: A Scalable Dynamics Framework APEX Clothing
More informationDetermining the Cost Function In Tic-Tac-Toe puzzle game by Using Branch and Bound Algorithm
Determining the Cost Function In Tic-Tac-Toe puzzle game by Using Branch and Bound Algorithm Teofebano - 13512050 Program Studi Teknik Informatika Sekolah Teknik Elektro dan Informatika Institut Teknologi
More informationGlobal Social Casino Market: Size, Trends & Forecasts ( ) March 2018
Global Social Casino Market: Size, Trends & Forecasts (2018-2022) March 2018 Global Social Casino Market: Coverage Executive Summary and Scope Introduction/Market Overview Global Market Analysis Regional
More informationThe Mathematics of Playing Tic Tac Toe
The Mathematics of Playing Tic Tac Toe by David Pleacher Although it has been shown that no one can ever win at Tic Tac Toe unless a player commits an error, the game still seems to have a universal appeal.
More informationLecture 33: How can computation Win games against you? Chess: Mechanical Turk
4/2/0 CS 202 Introduction to Computation " UNIVERSITY of WISCONSIN-MADISON Computer Sciences Department Lecture 33: How can computation Win games against you? Professor Andrea Arpaci-Dusseau Spring 200
More informationTutorial: The Web of Things
Tutorial: The Web of Things Carolina Fortuna 1, Marko Grobelnik 2 1 Communication Systems Department, 2 Artificial Intelligence Laboratory Jozef Stefan Institute, Jamova 39, 1000 Ljubljana, Slovenia {carolina.fortuna,
More informationAuthoring & Delivering MR Experiences
Authoring & Delivering MR Experiences Matthew O Connor 1,3 and Charles E. Hughes 1,2,3 1 School of Computer Science 2 School of Film and Digital Media 3 Media Convergence Laboratory, IST University of
More informationR&D Activities at the UCI Center for Computer Games and Virtual Worlds
R&D Activities at the UCI Center for Computer Games and Virtual Worlds Walt Scacchi and others Institute for Software Research and Center for Computer Games and Virtual Worlds University of California,
More informationCS 188: Artificial Intelligence
CS 188: Artificial Intelligence Adversarial Search Instructor: Stuart Russell University of California, Berkeley Game Playing State-of-the-Art Checkers: 1950: First computer player. 1959: Samuel s self-taught
More informationCS188: Artificial Intelligence, Fall 2011 Written 2: Games and MDP s
CS88: Artificial Intelligence, Fall 20 Written 2: Games and MDP s Due: 0/5 submitted electronically by :59pm (no slip days) Policy: Can be solved in groups (acknowledge collaborators) but must be written
More informationCS 188: Artificial Intelligence Spring Announcements
CS 188: Artificial Intelligence Spring 2011 Lecture 7: Minimax and Alpha-Beta Search 2/9/2011 Pieter Abbeel UC Berkeley Many slides adapted from Dan Klein 1 Announcements W1 out and due Monday 4:59pm P2
More informationAnnouncements. CS 188: Artificial Intelligence Spring Game Playing State-of-the-Art. Overview. Game Playing. GamesCrafters
CS 188: Artificial Intelligence Spring 2011 Announcements W1 out and due Monday 4:59pm P2 out and due next week Friday 4:59pm Lecture 7: Mini and Alpha-Beta Search 2/9/2011 Pieter Abbeel UC Berkeley Many
More informationAdversarial Search and Game- Playing C H A P T E R 6 C M P T : S P R I N G H A S S A N K H O S R A V I
Adversarial Search and Game- Playing C H A P T E R 6 C M P T 3 1 0 : S P R I N G 2 0 1 1 H A S S A N K H O S R A V I Adversarial Search Examine the problems that arise when we try to plan ahead in a world
More information2018 Research Campaign Descriptions Additional Information Can Be Found at
2018 Research Campaign Descriptions Additional Information Can Be Found at https://www.arl.army.mil/opencampus/ Analysis & Assessment Premier provider of land forces engineering analyses and assessment
More informationIndependent Communications Authority of South Africa Pinmill Farm, 164 Katherine Street, Sandton Private Bag X10002, Sandton, 2146
Independent Communications Authority of South Africa Pinmill Farm, 164 Katherine Street, Sandton Private Bag X10002, Sandton, 2146 ANNEXURE A TECHNICAL SPECIFICATIONS ICASA 09/2018 1. Purpose of the Request
More informationAGENTLESS ARCHITECTURE
ansible.com +1 919.667.9958 WHITEPAPER THE BENEFITS OF AGENTLESS ARCHITECTURE A management tool should not impose additional demands on one s environment in fact, one should have to think about it as little
More informationAI Approaches to Ultimate Tic-Tac-Toe
AI Approaches to Ultimate Tic-Tac-Toe Eytan Lifshitz CS Department Hebrew University of Jerusalem, Israel David Tsurel CS Department Hebrew University of Jerusalem, Israel I. INTRODUCTION This report is
More informationCOMP9414: Artificial Intelligence Problem Solving and Search
CMP944, Monday March, 0 Problem Solving and Search CMP944: Artificial Intelligence Problem Solving and Search Motivating Example You are in Romania on holiday, in Arad, and need to get to Bucharest. What
More informationFULL MISSION REHEARSAL & SIMULATION SOLUTIONS
FULL MISSION REHEARSAL & SIMULATION SOLUTIONS COMPLEX & CHANGING MISSIONS. REDUCED TRAINING BUDGETS. BECAUSE YOU OPERATE IN A NETWORK-CENTRIC ENVIRONMENT YOU SHOULD BE TRAINED IN ONE. And like your missions,
More informationS&T Stakeholders Conference
S&T Stakeholders Conference May 21-24, 2007 Future Attribute Screening Technology Mobile Module (FAST M 2 ) Innovation/HSARPA HIP Bob Burns Program Manager Office of Innovation/Human Factors Division Science
More informationKnowledge Management for Command and Control
Knowledge Management for Command and Control Dr. Marion G. Ceruti, Dwight R. Wilcox and Brenda J. Powers Space and Naval Warfare Systems Center, San Diego, CA 9 th International Command and Control Research
More informationSYNCHROPHASOR TECHNOLOGY GLOSSARY Revision Date: April 24, 2011
SYNCHROPHASOR TECHNOLOGY GLOSSARY Revision Date: April 24, 2011 Baselining using large quantities of historical phasor data to identify and understand patterns in interconnection-wide grid behavior, to
More informationConversion Masters in IT (MIT) AI as Representation and Search. (Representation and Search Strategies) Lecture 002. Sandro Spina
Conversion Masters in IT (MIT) AI as Representation and Search (Representation and Search Strategies) Lecture 002 Sandro Spina Physical Symbol System Hypothesis Intelligent Activity is achieved through
More informationInSciTe Adaptive: Intelligent Technology Analysis Service Considering User Intention
InSciTe Adaptive: Intelligent Technology Analysis Service Considering User Intention Jinhyung Kim, Myunggwon Hwang, Do-Heon Jeong, Sa-Kwang Song, Hanmin Jung, Won-kyung Sung Korea Institute of Science
More informationExploring Technology 8 th Grade Prof Crudele
Exploring Technology 8 th Grade Prof Crudele Exploring Technology is an introductory course covering many important topics and concepts in computer science. Students are evaluated as follows: 15% HW/CW,
More information2014 ARO-MURI Cyber Situation Awareness Review University of California at Santa Barbara, November 19,
2014 ARO-MURI Cyber Situation Awareness Review University of California at Santa Barbara, November 19, 2014 1 1 Correlation Engine COAs Data Data Data Data Real World Enterprise Network Mission Cyber-Assets
More informationPRESS RELEASE EUROSATORY 2018
PRESS RELEASE EUROSATORY 2018 Booth Hall 5 #B367 June 2018 Press contact: Emmanuel Chiva chiva@agueris.com #+33 6 09 76 66 81 www.agueris.com SUMMARY Who we are Our solutions: Generic Virtual Trainer Embedded
More informationGame Playing AI Class 8 Ch , 5.4.1, 5.5
Game Playing AI Class Ch. 5.-5., 5.4., 5.5 Bookkeeping HW Due 0/, :59pm Remaining CSP questions? Cynthia Matuszek CMSC 6 Based on slides by Marie desjardin, Francisco Iacobelli Today s Class Clear criteria
More informationDefense Security Service Industrial Security Field Operations
NAO Presentation Impact 2017 April 25, 2017 Defense Security Service Industrial Security Field Operations Karl Hellmann Assistant Deputy Director, NISP Authorization Office (NAO) NAO Topics RMF Overview
More informationDigital Engineering Support to Mission Engineering
21 st Annual National Defense Industrial Association Systems and Mission Engineering Conference Digital Engineering Support to Mission Engineering Philomena Zimmerman Dr. Judith Dahmann Office of the Under
More informationSENDORA: Design of wireless sensor network aided cognitive radio systems
SEVENTH FRAMEWORK PROGRAMME THEME ICT-2007-1.1 The Network of the Future Project 216076 SENDORA: Design of wireless sensor network aided cognitive radio systems Pål Grønsund, TELENOR WInnComm, Brussels,
More informationAdversarial Search 1
Adversarial Search 1 Adversarial Search The ghosts trying to make pacman loose Can not come up with a giant program that plans to the end, because of the ghosts and their actions Goal: Eat lots of dots
More informationBoard Game AIs. With a Focus on Othello. Julian Panetta March 3, 2010
Board Game AIs With a Focus on Othello Julian Panetta March 3, 2010 1 Practical Issues Bug fix for TimeoutException at player init Not an issue for everyone Download updated project files from CS2 course
More informationAdversarial Search: Game Playing. Reading: Chapter
Adversarial Search: Game Playing Reading: Chapter 6.5-6.8 1 Games and AI Easy to represent, abstract, precise rules One of the first tasks undertaken by AI (since 1950) Better than humans in Othello and
More informationRobotic Systems. Jeff Jaster Deputy Associate Director for Autonomous Systems US Army TARDEC Intelligent Ground Systems
Robotic Systems Jeff Jaster Deputy Associate Director for Autonomous Systems US Army TARDEC Intelligent Ground Systems Robotics Life Cycle Mission Integrate, Explore, and Develop Robotics, Network and
More informationFault analysis framework. Ana Gainaru, Franck Cappello, Bill Kramer
Fault analysis framework Ana Gainaru, Franck Cappello, Bill Kramer Third Workshop of the INRIA Illinois Joint Laboratory on Petascale Computing, Bordeaux June 22 24 2010 Contents Introduction Framework
More informationSpatial Analysis with ArcGIS Pro. Krithica Kantharaj, Esri
Spatial Analysis with ArcGIS Pro Krithica Kantharaj, Esri What is analysis? Analysis transforms raw data into information or knowledge Spatial analysis does this for geographic or spatial data Who? What?
More informationCS 771 Artificial Intelligence. Adversarial Search
CS 771 Artificial Intelligence Adversarial Search Typical assumptions Two agents whose actions alternate Utility values for each agent are the opposite of the other This creates the adversarial situation
More informationRMF Considerations for Navy Industrial Control Systems Track 4 Session 2 Jeff Johnson Naval District Washington August [XX], 2017
RMF Considerations for Navy Industrial Control Systems Track 4 Session 2 RMF Considerations for Navy Industrial Control Systems Track 4 Session 2 Jeff Johnson Naval District Washington August [XX], 2017
More informationGame Description Logic and Game Playing
Game Description Logic and Game Playing Laurent Perrussel November 29 - Planning and Games workshop IRIT Université Toulouse Capitole 1 Motivation Motivation(1/2) Game: describe and justify actions in
More informationEmbedded Systems Lab
Embedded Systems Lab UNIVERSITY OF JORDAN Tic-Tac-Toe GAME PROJECT Embedded lab Engineers Page 1 of 5 Preferred Group Size Grading Project Due Date (2) Two is the allowed group size. The group can be from
More informationGame-playing AIs: Games and Adversarial Search FINAL SET (w/ pruning study examples) AIMA
Game-playing AIs: Games and Adversarial Search FINAL SET (w/ pruning study examples) AIMA 5.1-5.2 Games: Outline of Unit Part I: Games as Search Motivation Game-playing AI successes Game Trees Evaluation
More informationA Hybrid Risk Management Process for Interconnected Infrastructures
A Hybrid Management Process for Interconnected Infrastructures Stefan Schauer Workshop on Novel Approaches in and Security Management for Critical Infrastructures Vienna, 19.09.2017 Contents Motivation
More information