Interactive Visualizations for Cyber-

Size: px
Start display at page:

Download "Interactive Visualizations for Cyber-"

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! 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 information

ARTIFICIAL INTELLIGENCE (CS 370D)

ARTIFICIAL 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 information

mywbut.com Two agent games : alpha beta pruning

mywbut.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 information

CSC 110 Lab 4 Algorithms using Functions. Names:

CSC 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 information

Game-playing AIs: Games and Adversarial Search I AIMA

Game-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 information

Module 3. Problem Solving using Search- (Two agent) Version 2 CSE IIT, Kharagpur

Module 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 information

Adversarial 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 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 information

2359 (i.e. 11:59:00 pm) on 4/16/18 via Blackboard

2359 (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 information

Shared 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 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 information

Sven Wachsmuth Bielefeld University

Sven 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 information

CS 2710 Foundations of AI. Lecture 9. Adversarial search. CS 2710 Foundations of AI. Game search

CS 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 information

CMPUT 396 Tic-Tac-Toe Game

CMPUT 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 information

CS 331: Artificial Intelligence Adversarial Search II. Outline

CS 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 information

Artificial Intelligence

Artificial 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 information

Future 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 Future of Cities Harvard GSD Smart[er] Citizens Bergamo University SMART[ER] CITIES Harvard Graduate School of Design SCI 0637100 Spring

More information

Set 4: Game-Playing. ICS 271 Fall 2017 Kalev Kask

Set 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 information

The LVCx Framework. The LVCx Framework An Advanced Framework for Live, Virtual and Constructive Experimentation

The 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 information

CS 1571 Introduction to AI Lecture 12. Adversarial search. CS 1571 Intro to AI. Announcements

CS 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 information

Tic-tac-toe. Lars-Henrik Eriksson. Functional Programming 1. Original presentation by Tjark Weber. Lars-Henrik Eriksson (UU) Tic-tac-toe 1 / 23

Tic-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 information

NVIDIA 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 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 information

MOBILIZE AND MAXIMIZE THE POTENTIAL OF P25 DIGITAL LMR

MOBILIZE 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 information

TIES: An Engineering Design Methodology and System

TIES: 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 information

Ubiquitous Home Simulation Using Augmented Reality

Ubiquitous 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 information

Architecting Systems of the Future, page 1

Architecting Systems of the Future, page 1 Architecting Systems of the Future featuring Eric Werner interviewed by Suzanne Miller ---------------------------------------------------------------------------------------------Suzanne Miller: Welcome

More information

A CYBER PHYSICAL SYSTEMS APPROACH FOR ROBOTIC SYSTEMS DESIGN

A 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 information

CS 4700: Foundations of Artificial Intelligence

CS 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 information

Announcements. Homework 1 solutions posted. Test in 2 weeks (27 th ) -Covers up to and including HW2 (informed search)

Announcements. 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 information

Exploring Virtual Reality (VR) with ArcGIS. Euan Cameron Simon Haegler Mark Baird

Exploring 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 information

Generating Virtual Environments by Linking Spatial Data Processing with a Gaming Engine

Generating 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 information

Project Example: wissen.de

Project 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 information

Adversarial 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 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 information

An Agent-based Heterogeneous UAV Simulator Design

An 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 information

MSc(CompSc) List of courses offered in

MSc(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 information

Rules of the game. chess checkers tic-tac-toe...

Rules 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 information

MOBILIZE 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 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 information

Playing Games. Henry Z. Lo. June 23, We consider writing AI to play games with the following properties:

Playing 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 information

Autonomous Robotic (Cyber) Weapons?

Autonomous 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 information

1 Introduction. 1.1 Game play. CSC 261 Lab 4: Adversarial Search Fall Assigned: Tuesday 24 September 2013

1 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 information

Real-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 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 information

Distributed Robotics: Building an environment for digital cooperation. Artificial Intelligence series

Distributed 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 information

Expression Of Interest

Expression 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 information

Unit 12: Artificial Intelligence CS 101, Fall 2018

Unit 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 information

Artificial Intelligence Lecture 3

Artificial 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 information

Mission 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 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 information

Game Tree Search 1/6/17

Game 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 information

Rules of the game. chess checkers tic-tac-toe...

Rules 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 information

2 person perfect information

2 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 information

TECHNOLOGY 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 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 information

Industry 4.0: the new challenge for the Italian textile machinery industry

Industry 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 information

Autonomous Control for Unmanned

Autonomous 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 information

Combining complementary skills, research, novel technologies.

Combining 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 information

Development of CBRN Impact Assessment Capabilities

Development 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 information

Solving 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 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 information

Edward Waller Joseph Chaput Presented at the IAEA International Conference on Physical Protection of Nuclear Material and Facilities

Edward 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 information

Ar#ficial)Intelligence!!

Ar#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 information

Activity-Centric Configuration Work in Nomadic Computing

Activity-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 information

Game-Playing & Adversarial Search

Game-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 information

NVIDIA 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 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 information

Determining 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 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 information

Global Social Casino Market: Size, Trends & Forecasts ( ) March 2018

Global 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 information

The Mathematics of Playing Tic Tac Toe

The 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 information

Lecture 33: How can computation Win games against you? Chess: Mechanical Turk

Lecture 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 information

Tutorial: The Web of Things

Tutorial: 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 information

Authoring & Delivering MR Experiences

Authoring & 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 information

R&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 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 information

CS 188: Artificial Intelligence

CS 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 information

CS188: Artificial Intelligence, Fall 2011 Written 2: Games and MDP s

CS188: 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 information

CS 188: Artificial Intelligence Spring Announcements

CS 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 information

Announcements. CS 188: Artificial Intelligence Spring Game Playing State-of-the-Art. Overview. Game Playing. GamesCrafters

Announcements. 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 information

Adversarial 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 : 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 information

2018 Research Campaign Descriptions Additional Information Can Be Found at

2018 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 information

Independent 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 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 information

AGENTLESS ARCHITECTURE

AGENTLESS 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 information

AI Approaches to Ultimate Tic-Tac-Toe

AI 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 information

COMP9414: Artificial Intelligence Problem Solving and Search

COMP9414: 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 information

FULL MISSION REHEARSAL & SIMULATION SOLUTIONS

FULL 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 information

S&T Stakeholders Conference

S&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 information

Knowledge Management for Command and Control

Knowledge 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 information

SYNCHROPHASOR TECHNOLOGY GLOSSARY Revision Date: April 24, 2011

SYNCHROPHASOR 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 information

Conversion 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 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 information

InSciTe Adaptive: Intelligent Technology Analysis Service Considering User Intention

InSciTe 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 information

Exploring Technology 8 th Grade Prof Crudele

Exploring 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 information

2014 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 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 information

PRESS RELEASE EUROSATORY 2018

PRESS 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 information

Game Playing AI Class 8 Ch , 5.4.1, 5.5

Game 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 information

Defense Security Service Industrial Security Field Operations

Defense 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 information

Digital Engineering Support to Mission Engineering

Digital 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 information

SENDORA: Design of wireless sensor network aided cognitive radio systems

SENDORA: 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 information

Adversarial Search 1

Adversarial 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 information

Board 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 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 information

Adversarial Search: Game Playing. Reading: Chapter

Adversarial 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 information

Robotic 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 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 information

Fault analysis framework. Ana Gainaru, Franck Cappello, Bill Kramer

Fault 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 information

Spatial Analysis with ArcGIS Pro. Krithica Kantharaj, Esri

Spatial 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 information

CS 771 Artificial Intelligence. Adversarial Search

CS 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 information

RMF 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 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 information

Game Description Logic and Game Playing

Game 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 information

Embedded Systems Lab

Embedded 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 information

Game-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 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 information

A Hybrid Risk Management Process for Interconnected Infrastructures

A 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