Character AI: Sensing & Perception
|
|
- Audrey Carter
- 5 years ago
- Views:
Transcription
1 Lecture 22 Character AI:
2 Take Away for Today Sensing as primary bottleneck Why is sensing so problematic? What types of things can we do to improve it? Optimized sense computation Can we improve sense computation performance? Can we share sensing between NPCs? Sense event matching What are events and how are y represented? What is advantage of an event system? 2
3 Review: Sense-Think-Act Sense: Perceive world Reading game state Example: enemy near? Think: Choose an action Often merged with sense Example: fight or flee Act: Update state Simple and fast Example: reduce health? 3
4 Recall: Sensing Performance Sensing may be slow! Consider all objects Example: morale n knights, n skeletons Knights fear skeletons Proportional to # seen Count skeletons in view O(n) to count skeletons O(n 2 ) for all units Time per tick 3 units 2 units 1 unit 4
5 Recall: Sensing Performance Sensing may be slow! Consider all objects Example: morale n knights, n skeletons Knights fear skeletons Proportional to # seen Count skeletons in view O(n) to count skeletons O(n 2 ) for all units Time per tick 3 units 2 units 1 unit 5
6 Aggregation Idea taken from databases Unordered set of information Combine into single value Used in statistical analysis Examples: sum, avg, mode Decomposable Aggregates Split set up into subsets Aggregate on each subset Combine values from subsets Only for some aggregates avg = 8 (16,2) avg = 6 (12,2) avg = 8 (16,2) avg = 6 (12,2) avg = 7 (28,4) avg = 7 (28,4) avg = 7 (56,8) 6
7 Aggregation Idea taken from databases Unordered set of information Combine into single value Used in statistical analysis Examples: sum, avg, mode Decomposable Aggregates Split set up into subsets Aggregate on each subset Allows for fast parallel computation Combine values from subsets Only for some aggregates avg = 8 (16,2) avg = 6 (12,2) avg = 8 (16,2) avg = 6 (12,2) avg = 7 (28,4) avg = 7 (28,4) avg = 7 (56,8) 7
8 AI and Aggregation Trees Slide courtesy of Dave Mark Number of Allies Strength of Allies Number of Enemies Strength of Enemies Allied Strength Threat Ratio Enemy Strength Proximity to Base My Health Proximity to Leader Urgency My Morale Retreat % 8
9 AI and Aggregation Trees Slide courtesy of Dave Mark Number of Allies Strength of Allies Number of Enemies Strength of Enemies Map-Reduce Pipeline Allied Strength Do all sides at once using key-value pairs Threat Ratio Enemy Strength Urgency Proximity to Base My Health My Morale Proximity to Leader Retreat % 9
10 Influence Maps: Pathfinding and AI B A Slide courtesy of Dave Mark 10
11 Implementing Influence Maps Use pathfinding grid Track movement in square Track if friend or foe Keep count as a queue Count is sum of queue A B 1 0 Allows us to time out 1 0 Orwise, marked forever 1 Use queue as a predictor Look at rate of change Also valuable for AI Sensing is at grid, not NPC 11
12 Advantages of Influence Maps B A Influence data reflects changes Slide courtesy of Dave Mark 12
13 Advantages of Influence Maps B Add a priori assumptions A Slide courtesy of Dave Mark 13
14 Sensing: Perception Groups Vision: limited field of view Gives exact object location, information Limited by obstacles and range Little information (motion) at periphery Sound: omni-directional Gives direction & distances Requires you track sounds actions make Smell: omni-directional No direction or distance; proximity only Requires you track smells actions make 14
15 Sensing: Line-of-Sight 15
16 Sensing: Line-of-Sight 16
17 3D Line-of-Sight: Ray Casting 17
18 3D Line-of-Sight: Ray Casting 18
19 3D Line-of-Sight: Ray Casting 19
20 Sense Events Event: encoded sense data Tagged with sense type Pre-aggregated information Information self-contained Object with methods Class is sense type Sensing is event matching Each event has a type NPCs register for a type Send NPC registered events Check if event is relevant! Event: CharacterExposed! boolean isseen(npc guard); float distanceto(npc guard); Vector movedirection(); 20
21 Sense Event Matching sound sight sound Register events of interest Event Handler Game Loop sound smell 21
22 Sense Event Matching Notify of any matching events Event Handler Game Loop Check for any matching events 22
23 Sense Event Matching Notify of any matching events Event Handler Advantage: Sensing is Global, Not Local Game Loop Check for any matching events 23
24 Representing Events Lightweight Heavyweight Memory Player Memory Player CharacterExposed Reference to player CharacterExposed Copy of player 24
25 Representing Events Lightweight Heavyweight class Event {" Player ref;" Event(Player p) {" ref = p;" }" } Advantages Fast to create event No additional memory class Event {" Player ref;" Event(Player p) {" ref = p.copy();" }" } Advantages Stores past events Can be communicated 25
26 Simple Pub/Sub Architecture NPC Hashtable Event types are keys Values are lists of NPCs! tag1 Say NPC subscribes to e Each update cycle Generate all of events tag2 Complex Pub/Sub Query System System Game Loop Get NPCs for each event tag1, tag3 Send those events to NPC Process NPCs normally tag4 26
27 Simple Pub/Sub Architecture tag1 NPC Behavior tag2 Complex Pub/Sub Query System System Game Loop tag1, tag3 tag4 27
28 Restrict to nearby NPCs Have detection range Limits events sensed Spatial Optimizations Easy to combine with event matching system Works in both directions Nimbus: can see radius Aura: can be seen radius Area of interest management 28
29 Case Study: Thief Series 29
30 30
31 Line-of-Sight in Thief Long Distance Focused View Motion Detection Peripheral Vision Short Distance 31
32 Sounds in Thief Easier than vision Primarily distance-based Decays probabilistically Tag with level of interest Sounds can be blocked Not same as line-of-sight Use alternate level map Or tag your visible map Not physically realistic Echoes? Reflections 32
33 Sounds in Thief Sounds are general purpose Resuable framework Code is lightweight Encodes or senses Example: Smell Treated as pseudo-sound Generate like any sound * sniff * Again, ignores or factors * sniff * Wind direction Masking smells 33
34 Representing Events Lightweight Heavyweight class Event {" Player ref;" Event(Player p) {" ref = p;" }" } Advantages Fast to create event No additional memory class Event {" Player ref;" Event(Player p) {" ref = p.copy();" }" } Advantages Stores past events Can be communicated 34
35 Communicating Senses First Hand LOS Sight & Sound First Hand LOS Sight & Sound Second Hand Sight & Sound? 35
36 Alertness: Active Senses High Alert First Hand LOS Sight & Sound Medium Alert First Hand LOS Sight & Sound Second Hand Sight & Sound High Alert First Hand Sound 36
37 Thief: Sense Events and Aggregation Position Lighting Movement Exposure Sound System Visibility Sound Queue Viewcone Selector Viewcone Non-specific Spatial Events Look Listen Sense Pulse Receiver Game Mechanics and Configuration Inter-Agent Communication Ramp Up Delay Cool-down Capacitor Inter-Agent Observation 37 Sense Links
38 Summary Sensing is most expensive part of AI Each character looks at every object in game Often leads to O(n 2 ) behavior (bad!) Can optimize sense garing Aggregation is amenable to parallelization Can piggyback some data onto pathfinding Event matching inverts sensing problem Creation of sense makes a data event Forward event to relevant NPCs 38
the gamedesigninitiative at cornell university Lecture 20 Pathfinding
Lecture 20 Take way for Today What are primary goals for pathfinding? Identify advantages/disadvantages of * In what situations does * fail (or look bad)? What can we do to fix se problems? Why combine
More informationPrinciples of Computer Game Design and Implementation. Lecture 23
Principles of Computer Game Design and Implementation Lecture 23 We already learned Decision Tree 2 Outline for today Finite state machine 3 Creating & Controlling AI Behaviors Behavior: A Sequence of
More informationSituational Awareness: Terrain Reasoning for Tactical Shooter A.I
Situational Awareness: Terrain Reasoning for Tactical Shooter A.I Situational Awareness The capability of an A.I. agent to reason is limited by the data its sensor's can acquire about the world The representation
More informationGraphs, Search, Pathfinding (behavior involving where to go) Steering, Flocking, Formations (behavior involving how to go)
Graphs, Search, Pathfinding (behavior involving where to go) Steering, Flocking, Formations (behavior involving how to go) Class N-2 1. What are some benefits of path networks? 2. Cons of path networks?
More informationCS 387/680: GAME AI MOVEMENT
CS 387/680: GAME AI MOVEMENT 4/5/2016 Instructor: Santiago Ontañón santi@cs.drexel.edu Class website: https://www.cs.drexel.edu/~santi/teaching/2016/cs387/intro.html Reminders Check Blackboard site for
More informationGame AI: The set of algorithms, representations, tools, and tricks that support the creation and management of real-time digital experiences
Game AI: The set of algorithms, representations, tools, and tricks that support the creation and management of real-time digital experiences : A rule of thumb, simplification, or educated guess that reduces
More informationTotal Recall: Automatic Query Expansion with a Generative Feature Model for Object Retrieval O. Chum, et al.
Total Recall: Automatic Query Expansion with a Generative Feature Model for Object Retrieval O. Chum, et al. Presented by Brandon Smith Computer Vision Fall 2007 Objective Given a query image of an object,
More informationWhere are we? Setup Sprites. Input. Collision Drawing Sprites. Initializing, loading, movement Parameters (color, alpha, scale, etc.
Setup Sprites Where are we? Initializing, loading, movement Parameters (color, alpha, scale, etc.) Input Get input and respond to input Collision Drawing Sprites Next Advanced movement Various types of
More informationHierarchical Dynamic Pathfinding for Large Voxel Worlds
Hierarchical Dynamic Pathfinding for Large Voxel Worlds Benoit Alain Lead Programmer & CTO, Sauropod Studio Hierarchical Dynamic Pathfinding for Large Voxel Worlds Hierarchical Dynamic Pathfinding for
More information12 - More Steering. from Milligan, "Artificial Intelligence for Games", Morgan Kaufman, 2006
12 - More Steering from Milligan, "Artificial Intelligence for Games", Morgan Kaufman, 2006 Separation commonly used in crowds, when all characters moving in roughly the same direction (repulsion steering)
More informationProblems with the Imperative 1 Method
Announcements Video Game AI: Lecture 13 Behavior Trees & Blackboards Nathan Sturtevant COMP 3705 HW #4 online Who is using the makefiles? Project online after class 4 lectures left after today Schedule
More informationThe Drone War. Motivation and Introduction: Project Proposal
The Drone War Project Proposal COMS W4115 Programming Languages and Translators Columbia University, Fall 2012 Professor: Stephen A. Edwards Students: George Brink gb2280 Xiaotong Chen xc2230 Shuo Qiu
More informationLocal Search Methods. CS 188: Artificial Intelligence Fall Announcements. Hill Climbing. Hill Climbing Diagram. Today
CS 188: Artificial Intelligence Fall 2006 Lecture 5: Robot Motion Planning 9/14/2006 Local Search Methods Queue-based algorithms keep fallback options (backtracking) Local search: improve what you have
More informationAnnouncements. CS 188: Artificial Intelligence Fall Robot motion planning! Today. Robotics Tasks. Mobile Robots
CS 188: Artificial Intelligence Fall 2007 Lecture 6: Robot Motion Planning 9/13/2007 Announcements Project 1 due (yesterday)! Project 2 (Pacman with ghosts) up in a few days Reminder: you are allowed to
More informationCS 188: Artificial Intelligence Fall Announcements
CS 188: Artificial Intelligence Fall 2007 Lecture 6: Robot Motion Planning 9/13/2007 Dan Klein UC Berkeley Many slides over the course adapted from either Stuart Russell or Andrew Moore Announcements Project
More informationSimulation: Particle Systems
Simulation: Particle Systems Course web page: http://goo.gl/eb3aa February 28, 2012 Lecture 5 Particle Systems Definition: Simulation of a set of similar, moving agents in a larger environment Scale usually
More informationWave Properties. Page 1 of 13
Wave Properties Transverse and longitudinal waves Be able to describe the difference between longitudinal and transverse waves including examples of each (e.g. ripples / light for transverse & sound (compression
More informationArtificial Intelligence
Artificial Intelligence Lecture 06 Sensor Systems Edirlei Soares de Lima Game AI Model Pathfinding Steering behaviours Finite state machines Automated planning Behaviour trees
More informationTactical and Strategic AI
DM842 Computer Game Programming: AI Lecture 8 Marco Chiarandini Department of Mathematics & Computer Science University of Southern Denmark Outline 1. Scripting Action Management 2. Waypoint Tactics Tactical
More informationEE795: Computer Vision and Intelligent Systems
EE795: Computer Vision and Intelligent Systems Spring 2012 TTh 17:30-18:45 FDH 204 Lecture 14 130307 http://www.ee.unlv.edu/~b1morris/ecg795/ 2 Outline Review Stereo Dense Motion Estimation Translational
More informationLECTURE 5. Announcements
LECTURE 5 Announcements Falling Behind? Talk to us You still pass the class if you hand in all projects at the end of the semester Hand in even if you think you won t satisfy the playtesting requirements
More informationFormations in flow fields
Formations in flow fields Rick van Meer June 11, 2015 1 Introduction Pathfinding and formations (i.e. an arrangement of agents) are both a part of artificial intelligence and can be used in games. However,
More informationLECTURE 7. Announcements
LECTURE 7 Announcements Minecraft 4 Feedback Looks good! A game that minimally involves platforms Not based on any game in particular Super Mario 64? Team Fortress 2? Completely up to you to make unique
More informationProgramming Game Al by Example
Programming Game Al by Example Mat Buckland Wordware Publishing, Inc. Contents Foreword Acknowledgments Introduction xiii xvii xix Chapter 7 A Math and Physics Primer 1 Mathematics 1 Cartesian Coordinates
More informationGPU-based Distributed Behavior Models with CUDA
GPU-based Distributed Behavior Models with CUDA Courtesy: YouTube, ISIS Lab, Universita degli Studi di Salerno Bradly Alicea Introduction Flocking: Reynolds boids algorithm. * models simple local behaviors
More informationBimodal Spatial Reasoning with Continuous Motion
Bimodal Spatial Reasoning with Continuous Motion Samuel Wintermute and John E. Laird University of Michigan 2260 Hayward St. Ann Arbor, MI 48109-2121 {swinterm, laird}@umich.edu Abstract Symbolic AI systems
More informationCS 378: Computer Game Technology
CS 378: Computer Game Technology Dynamic Path Planning, Flocking Spring 2012 University of Texas at Austin CS 378 Game Technology Don Fussell Dynamic Path Planning! What happens when the environment changes
More informationthe gamedesigninitiative at cornell university Lecture 9 Memory Management
Lecture 9 Gaming Memory Constraints Redux Wii-U Playstation 4 2GB of RAM 1GB dedicated to OS Shared with GPGPU 8GB of RAM Shared GPU, 8-core CPU OS footprint unknown 2 Two Main Concerns with Memory Getting
More informationMemory Management: High-Level Overview
Lecture 9 : High-Level Overview Gaming Memory (Last Generation) Playstation 3 256 MB RAM for system 256 MB for graphics card X-Box 360 512 MB RAM (unified) Nintendo Wii 88 MB RAM (unified) 24 MB for graphics
More informationRobot Mapping. A Short Introduction to the Bayes Filter and Related Models. Gian Diego Tipaldi, Wolfram Burgard
Robot Mapping A Short Introduction to the Bayes Filter and Related Models Gian Diego Tipaldi, Wolfram Burgard 1 State Estimation Estimate the state of a system given observations and controls Goal: 2 Recursive
More informationCS 480: GAME AI STEERING BEHAVIORS. 4/12/2012 Santiago Ontañón
CS 480: GAME AI STEERING BEHAVIORS 4/12/2012 Santiago Ontañón santi@cs.drexel.edu https://www.cs.drexel.edu/~santi/teaching/2012/cs480/intro.html Reminders Check BBVista site for the course regularly Also:
More informationCS 354R: Computer Game Technology
CS 354R: Computer Game Technology AI Fuzzy Logic and Neural Nets Fall 2018 Fuzzy Logic Philosophical approach Decisions based on degree of truth Is not a method for reasoning under uncertainty that s probability
More informationRobotics Tasks. CS 188: Artificial Intelligence Spring Manipulator Robots. Mobile Robots. Degrees of Freedom. Sensors and Effectors
CS 188: Artificial Intelligence Spring 2006 Lecture 5: Robot Motion Planning 1/31/2006 Dan Klein UC Berkeley Many slides from either Stuart Russell or Andrew Moore Motion planning (today) How to move from
More informationLECTURE 6. Announcements
LECTURE 6 Announcements Minecraft 3 Feedback Infinite worlds! Terrain looks good Gameplay not super varied Happy Birthday Hassan! The Voxel Engine You re done with your first collision engine! Environment
More informationSung-Eui Yoon ( 윤성의 )
Path Planning for Point Robots Sung-Eui Yoon ( 윤성의 ) Course URL: http://sglab.kaist.ac.kr/~sungeui/mpa Class Objectives Motion planning framework Classic motion planning approaches 2 3 Configuration Space:
More informationNotes. Video Game AI: Lecture 5 Planning for Pathfinding. Lecture Overview. Knowledge vs Search. Jonathan Schaeffer this Friday
Notes Video Game AI: Lecture 5 Planning for Pathfinding Nathan Sturtevant COMP 3705 Jonathan Schaeffer this Friday Planning vs localization We cover planning today Localization is just mapping a real-valued
More informationRobotics and Autonomous Systems
Robotics and Autonomous Systems Lecture 13: Navigation in LeJOS Richard Williams Department of Computer Science University of Liverpool 1 / 40 Today From navigation to motion control. 2 / 40 Navigation
More informationLecture 3 Finite State Machines
Lecture 3 Finite State Machines CMPS 146, Fall 2013 Josh McCoy Readings First three will be posted right after class. First lecture: 19-35 Decision Tree lecture: 293-309 FSM/today's lecture: 309-333 All
More informationArtificial Intelligence
Artificial Intelligence Lecture 05 Randomness and Probability Edirlei Soares de Lima Game AI Model Pathfinding Steering behaviours Finite state machines Automated planning Behaviour
More informationAddressing the Memory Wall
Lecture 26: Addressing the Memory Wall Parallel Computer Architecture and Programming CMU 15-418/15-618, Spring 2015 Tunes Cage the Elephant Back Against the Wall (Cage the Elephant) This song is for the
More informationPathfinding. Pathfinding Planned Movement
345 Ludic Computing Lecture 10 Pathfinding Simon Colton & Alison Pease Computational Creativity Group Department of Computing Imperial College London www.doc.ic.ac.uk/ccg 1 Pathfinding Planned Movement
More informationSpatial Data Structures
15-462 Computer Graphics I Lecture 17 Spatial Data Structures Hierarchical Bounding Volumes Regular Grids Octrees BSP Trees Constructive Solid Geometry (CSG) March 28, 2002 [Angel 8.9] Frank Pfenning Carnegie
More informationUCB CS61C : Machine Structures
inst.eecs.berkeley.edu/~cs61c UCB CS61C : Machine Structures Lecture 14 Caches III Lecturer SOE Dan Garcia Google Glass may be one vision of the future of post-pc interfaces augmented reality with video
More informationCS61C : Machine Structures
inst.eecs.berkeley.edu/~cs61c CS61C : Machine Structures CS61C L22 Caches II (1) CPS today! Lecture #22 Caches II 2005-11-16 There is one handout today at the front and back of the room! Lecturer PSOE,
More informationShading, lighting, & BRDF Theory. Cliff Lindsay, PHD
Shading, lighting, & BRDF Theory Cliff Lindsay, PHD Overview of today s lecture BRDF Characteristics Lights in terms of BRDFs Classes of BRDFs Ambient light & Shadows in terms of BRDFs Decomposing Reflection
More informationARTIFICIAL INTELLIGENCE: SITUATIONAL AWARENESS
ARTIFICIAL INTELLIGENCE: SITUATIONAL AWARENESS So first of all before we start, we have to ask our self: What s is situational awareness exactly. A Quick google search reveals that: WHAT IS SITUATIONAL
More information1" 0" d" c" b" a" ...! ! Benefits of Larger Block Size. " Very applicable with Stored-Program Concept " Works well for sequential array accesses
Asst. Proflecturer SOE Miki Garcia inst.eecs.berkeley.edu/~cs61c UCB CS61C : Machine Structures Lecture 14 Caches III WHEN FIBER OPTICS IS TOO SLOW 07/16/2014: Wall Street Buys NATO Microwave Towers in
More informationUCB CS61C : Machine Structures
inst.eecs.berkeley.edu/~cs61c UCB CS61C : Machine Structures Lecture 14 Caches III Asst. Proflecturer SOE Miki Garcia WHEN FIBER OPTICS IS TOO SLOW 07/16/2014: Wall Street Buys NATO Microwave Towers in
More informationCongestion Control In the Network
Congestion Control In the Network Brighten Godfrey cs598pbg September 9 2010 Slides courtesy Ion Stoica with adaptation by Brighten Today Fair queueing XCP Announcements Problem: no isolation between flows
More informationAutomatic Scaling Iterative Computations. Aug. 7 th, 2012
Automatic Scaling Iterative Computations Guozhang Wang Cornell University Aug. 7 th, 2012 1 What are Non-Iterative Computations? Non-iterative computation flow Directed Acyclic Examples Batch style analytics
More informationPlatform Games Drawing Sprites & Detecting Collisions
Platform Games Drawing Sprites & Detecting Collisions Computer Games Development David Cairns Contents Drawing Sprites Collision Detection Animation Loop Introduction 1 Background Image - Parallax Scrolling
More informationParticle Systems. Typical Time Step. Particle Generation. Controlling Groups of Objects: Particle Systems. Flocks and Schools
Particle Systems Controlling Groups of Objects: Particle Systems Flocks and Schools A complex, fuzzy system represented by a large collection of individual elements. Each element has simple behavior and
More informationSpatial Data Structures
15-462 Computer Graphics I Lecture 17 Spatial Data Structures Hierarchical Bounding Volumes Regular Grids Octrees BSP Trees Constructive Solid Geometry (CSG) April 1, 2003 [Angel 9.10] Frank Pfenning Carnegie
More informationDamaging, Attacking and Interaction
Damaging, Attacking and Interaction In this tutorial we ll go through some ways to add damage, health and interaction to our scene, as always this isn t the only way, but it s the way I will show you.
More informationI may not have gone where I intended to go, but I think I have ended up where I needed to be. Douglas Adams
Disclaimer: I use these notes as a guide rather than a comprehensive coverage of the topic. They are neither a substitute for attending the lectures nor for reading the assigned material. I may not have
More informationNon-Homogeneous Swarms vs. MDP s A Comparison of Path Finding Under Uncertainty
Non-Homogeneous Swarms vs. MDP s A Comparison of Path Finding Under Uncertainty Michael Comstock December 6, 2012 1 Introduction This paper presents a comparison of two different machine learning systems
More informationCourse Outline. Video Game AI: Lecture 7 Heuristics & Smoothing. Finding the best location out of several. Variable size units / special movement?
Course Outline Video Game AI: Lecture 7 Heuristics & Smoothing Nathan Sturtevant COMP 3705 http://aigamedev.com/ now has free interviews! Miscellaneous details Heuristics How better heuristics can be built
More informationECE276B: Planning & Learning in Robotics Lecture 5: Configuration Space
ECE276B: Planning & Learning in Robotics Lecture 5: Configuration Space Lecturer: Nikolay Atanasov: natanasov@ucsd.edu Teaching Assistants: Tianyu Wang: tiw161@eng.ucsd.edu Yongxi Lu: yol070@eng.ucsd.edu
More informationChapter Seven Morgan Kaufmann Publishers
Chapter Seven Memories: Review SRAM: value is stored on a pair of inverting gates very fast but takes up more space than DRAM (4 to 6 transistors) DRAM: value is stored as a charge on capacitor (must be
More informationthe gamedesigninitiative at cornell university Lecture 12 Architecture Design
Lecture 12 Take Away for Today What should lead programmer do? How do CRC cards aid software design? What goes on each card? How do you lay m out? What properties should y have? How do activity diagrams
More informationRay Tracing III. Wen-Chieh (Steve) Lin National Chiao-Tung University
Ray Tracing III Wen-Chieh (Steve) Lin National Chiao-Tung University Shirley, Fundamentals of Computer Graphics, Chap 10 Doug James CG slides, I-Chen Lin s CG slides Ray-tracing Review For each pixel,
More informationUCB CS61C : Machine Structures
inst.eecs.berkeley.edu/~cs61c UCB CS61C : Machine Structures Lecture 32 Caches III 2008-04-16 Lecturer SOE Dan Garcia Hi to Chin Han from U Penn! Prem Kumar of Northwestern has created a quantum inverter
More informationCS 4649/7649 Robot Intelligence: Planning
CS 4649/7649 Robot Intelligence: Planning Probabilistic Roadmaps Sungmoon Joo School of Interactive Computing College of Computing Georgia Institute of Technology S. Joo (sungmoon.joo@cc.gatech.edu) 1
More informationCS 343H: Artificial Intelligence
CS 343H: Artificial Intelligence Lecture 4: Informed Search 1/23/2014 Slides courtesy of Dan Klein at UC-Berkeley Unless otherwise noted Today Informed search Heuristics Greedy search A* search Graph search
More informationEE382N (20): Computer Architecture - Parallelism and Locality Spring 2015 Lecture 14 Parallelism in Software I
EE382 (20): Computer Architecture - Parallelism and Locality Spring 2015 Lecture 14 Parallelism in Software I Mattan Erez The University of Texas at Austin EE382: Parallelilsm and Locality, Spring 2015
More informationFinding a Path. Advanced Pathfinding. Finding a Path. A* Pathfinding Search. Practical Path Planning 3/21/2016. Tile-Based Navigation Graphs IMGD 4000
3/21/2016 Finding a Path Often seems obvious and natural in real life e.g., Get from point A to B go around lake Advanced Pathfinding http://www.rocket5studios.com/tutorials/make-a-2d-game-withunity3d-using-only-free-tools-beginning-enemy-ai-with-a-pathfinding/
More informationTable of Contents 1.1. Introduction Installation Quick Start Documentation Asynchronous Configuration 1.4.
Table of Contents Introduction 1 Installation 2 Quick Start 3 Documentation Asynchronous Configuration Level Streaming Saving And Loading Slot Templates 1.1 1.2 1.3 1.4 1.4.1 1.4.2 1.4.3 1.4.4 1.4.5 1
More informationSplunk Review. 1. Introduction
Splunk Review 1. Introduction 2. Splunk Splunk is a software tool for searching, monitoring and analysing machine generated data via web interface. It indexes and correlates real-time and non-real-time
More informationAcquisitional Query Processing in TinyDB
Acquisitional Query Processing in TinyDB Sam Madden UC Berkeley NEST Winter Retreat 2003 1 Acquisitional Query Processing (ACQP) Cynical DB person question: what s really different about sensor networks?
More informationFeature selection. LING 572 Fei Xia
Feature selection LING 572 Fei Xia 1 Creating attribute-value table x 1 x 2 f 1 f 2 f K y Choose features: Define feature templates Instantiate the feature templates Dimensionality reduction: feature selection
More informationthe gamedesigninitiative at cornell university Lecture 13 Architecture Design
Lecture 13 Take Away for Today What should lead programmer do? How do CRC cards aid software design? What goes on each card? How do you lay m out? What properties should y have? How do activity diagrams
More informationData Stream Processing
Data Stream Processing Part II 1 Data Streams (recap) continuous, unbounded sequence of items unpredictable arrival times too large to store locally one pass real time processing required 2 Reservoir Sampling
More informationThe Five Rooms Project
The Five Rooms Project The Assignment If an artist is given the task of graphically designing a surface, then he is also left to decide which creative processes will be active and which criteria will then
More information(a) (b) (c) Fig. 1. Omnidirectional camera: (a) principle; (b) physical construction; (c) captured. of a local vision system is more challenging than
An Omnidirectional Vision System that finds and tracks color edges and blobs Felix v. Hundelshausen, Sven Behnke, and Raul Rojas Freie Universität Berlin, Institut für Informatik Takustr. 9, 14195 Berlin,
More informationAI/ML IRL. Joshua Eckroth Chief Architect / Assistant Professor of Computer Science i2k Connect / Stetson University
AI/ML IRL Joshua Eckroth Chief Architect / Assistant Professor of Computer Science i2k Connect / Stetson University Certain, closed systems: Well-defined inputs (e.g., bounded integers) Well-defined transformations
More informationMettler Toledo Driver PTC Inc. All Rights Reserved.
2017 PTC Inc. All Rights Reserved. 2 Table of Contents 1 Table of Contents 2 3 Overview 3 Setup 4 Channel Properties General 5 Channel Properties Serial Communications 6 Channel Properties Write Optimizations
More informationCS 387: GAME AI TACTIC AND STRATEGY
CS 387: GAME AI TACTIC AND STRATEGY 5/16/2017 Instructor: Santiago Ontañón so367@drexel.edu Class website: https://www.cs.drexel.edu/~santi/teaching/2017/cs387/intro.htm Outline Projects 1/2 Tactic and
More informationRouting Protocol comparison
Routing Protocol comparison Introduction to routing Networks allow people to communicate, collaborate, and interact in many ways. Networks are used to access web pages, talk using IP telephones, participate
More informationI may not have gone where I intended to go, but I think I have ended up where I needed to be. Douglas Adams
Disclaimer: I use these notes as a guide rather than a comprehensive coverage of the topic. They are neither a substitute for attending the lectures nor for reading the assigned material. I may not have
More information(Lec 14) Placement & Partitioning: Part III
Page (Lec ) Placement & Partitioning: Part III What you know That there are big placement styles: iterative, recursive, direct Placement via iterative improvement using simulated annealing Recursive-style
More informationCaching Basics. Memory Hierarchies
Caching Basics CS448 1 Memory Hierarchies Takes advantage of locality of reference principle Most programs do not access all code and data uniformly, but repeat for certain data choices spatial nearby
More informationInformation Retrieval and Web Search Engines
Information Retrieval and Web Search Engines Lecture 7: Document Clustering December 4th, 2014 Wolf-Tilo Balke and José Pinto Institut für Informationssysteme Technische Universität Braunschweig The Cluster
More informationCMSC427 Advanced shading getting global illumination by local methods. Credit: slides Prof. Zwicker
CMSC427 Advanced shading getting global illumination by local methods Credit: slides Prof. Zwicker Topics Shadows Environment maps Reflection mapping Irradiance environment maps Ambient occlusion Reflection
More informationlecture 18 - ray tracing - environment mapping - refraction
lecture 18 - ray tracing - environment mapping - refraction Recall Ray Casting (lectures 7, 8) for each pixel (x,y) { cast a ray through that pixel into the scene, and find the closest surface along the
More informationHot topics and Open problems in Computational Geometry. My (limited) perspective. Class lecture for CSE 546,
Hot topics and Open problems in Computational Geometry. My (limited) perspective Class lecture for CSE 546, 2-13-07 Some slides from this talk are from Jack Snoeyink and L. Kavrakis Key trends on Computational
More information(Due to rounding, values below may be only approximate estimates.) We will supply these numbers as they become available.
Below, for each problem on this Midterm Exam, Perfect is the percentage of students who received full credit, Partial is the percentage who received partial credit, and Zero is the percentage who received
More informationMidterm Exam Solutions Amy Murphy 28 February 2001
University of Rochester Midterm Exam Solutions Amy Murphy 8 February 00 Computer Systems (CSC/56) Read before beginning: Please write clearly. Illegible answers cannot be graded. Be sure to identify all
More informationAI for Games Movement Behaviors
DM842 Computer Game Programming: AI Lecture 1 AI for Games Movement Behaviors Marco Chiarandini Department of Mathematics & Computer Science University of Southern Denmark Outline 1. 2. 3. 4. Seeking Wandering
More informationIntroduction to Data Mining
Introduction to Data Mining Lecture #14: Clustering Seoul National University 1 In This Lecture Learn the motivation, applications, and goal of clustering Understand the basic methods of clustering (bottom-up
More informationYokogawa Controller Driver PTC Inc. All Rights Reserved.
2017 PTC Inc. All Rights Reserved. 2 Table of Contents Yokogawa Controller Driver 1 Table of Contents 2 Yokogawa Controller Driver 8 Overview 8 Setup 8 Channel Properties General 10 Channel Properties
More informationTo Do. Advanced Computer Graphics. The Story So Far. Course Outline. Rendering (Creating, shading images from geometry, lighting, materials)
Advanced Computer Graphics CSE 190 [Spring 2015], Lecture 16 Ravi Ramamoorthi http://www.cs.ucsd.edu/~ravir To Do Assignment 3 milestone due May 29 Should already be well on way Contact us for difficulties
More informationBlock Size Tradeoff (1/3) Benefits of Larger Block Size. Lecture #22 Caches II Block Size Tradeoff (3/3) Block Size Tradeoff (2/3)
CS61C L22 Caches II (1) inst.eecs.berkeley.edu/~cs61c CS61C Machine Structures CPS today! Lecture #22 Caches II 25-11-16 There is one handout today at the front and back of the room! Lecturer PSOE, new
More informationCourse Outline. Advanced Computer Graphics. Animation. The Story So Far. Animation. To Do
Advanced Computer Graphics CSE 163 [Spring 2017], Lecture 18 Ravi Ramamoorthi http://www.cs.ucsd.edu/~ravir 3D Graphics Pipeline Modeling (Creating 3D Geometry) Course Outline Rendering (Creating, shading
More informationSensor Modalities. Sensor modality: Different modalities:
Sensor Modalities Sensor modality: Sensors which measure same form of energy and process it in similar ways Modality refers to the raw input used by the sensors Different modalities: Sound Pressure Temperature
More informationPart 11: Collaborative Filtering. Francesco Ricci
Part : Collaborative Filtering Francesco Ricci Content An example of a Collaborative Filtering system: MovieLens The collaborative filtering method n Similarity of users n Methods for building the rating
More informationCrowdMixer: Multiple Agent Types in Situation-Based Crowd Simulations
CrowdMixer: Multiple Agent Types in Situation-Based Crowd Simulations Shannon Blyth and Howard J. Hamilton Department of Computer Science University of Regina, Regina, SK, Canada S4S 0A2 {blyth1sh, hamilton}@cs.uregina.ca
More informationPrinciples of Computer Game Design and Implementation. Lecture 13
Principles of Computer Game Design and Implementation Lecture 13 We already knew Collision detection overlap test and intersection test Detailed view Mid-level view 2 Outline for today High-level view
More informationMudd Adventure. A 3D Raycasting Game. CSEE 4840 Embedded Systems. Project Design 3/27/2014
Mudd Adventure A 3D Raycasting Game CSEE 4840 Embedded Systems Project Design 3/27/2014 Mingrui Xu(mx2151) Wei Cao (wc2467) Bowen Dang (bd2384) Shijie Hu (sh3251) Table of Contents Introduction... 3 Hardware...
More informationClass Overview. Introduction to Artificial Intelligence COMP 3501 / COMP Lecture 2. Problem Solving Agents. Problem Solving Agents: Assumptions
Class Overview COMP 3501 / COMP 4704-4 Lecture 2 Prof. JGH 318 Problem Solving Agents Problem Solving Agents: Assumptions Requires a goal Assume world is: Requires actions Observable What actions? Discrete
More informationExperiencing MIS, 6e (Kroenke) Chapter 2 Business Processes, Information Systems, and Information
Experiencing MIS, 6e (Kroenke) Chapter 2 Business Processes, Information Systems, and Information 1) Members of a team in a company need to understand business processes since they need to evaluate new
More information