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GI/acm-RegionalgruppeBremen/Oldenburg12. Februar 2002
Ubbo Visser
Soccer: Benchmark for ArtificialIntelligence Methods and Robotics
Overview
Introduction to RoboCupArtificial IntelligenceRobotic Soccer and Artificial IntelligenceRemarks
Introduction to RoboCup
Hypothesis
“By the year 2050, develop ateam of fully autonomoushumanoid robots that can winagainst the human world soccerchampions.”
Hiroaki Kitano, 1999President of the RoboCupFederation
Introduction to RoboCup
The RoboCup Federation
GeneralInternational research &educationFoster AI and intelligentroboticsProviding standard problemIntegrate a wide range oftechnologiesSoccer as primary domain
TechnologiesDesign principles ofautonomous agentsMulti-agent collaborationStrategy acquisitionReal-time reasoningRoboticsSensor fusion
Introduction to RoboCup
RoboCup: Areas of interest
* Multi-Agent/Robot Systems * Robotics, Science Education* Sensor/Motor Control * Adversarial Planning* Self-localization and Navigation * Planning, Reasoning, and Modeling* Vision and Image-Processing * Learning and Adaptive Systems* Cooperation and Collaboration * Simulation and Visualization* Realtime and Concurrent Programming * Embedded and Mobile Hardware* Non-conventional actuation systems * Artificial muscles* Next generation sensors for robotics * Mobile Robots and Humanoids* Search and rescue robots * Adjustable Autonomy* Disaster rescue information systems * System integration* Computer and Robotic Entertainment * Speech Synthesis* Natural Language Generation * Distributed Sensor Fusion* Omnidirectional Vision * Smart Materials* Fuel Cell Batteries * Software Engineering* Dynamic Resource Allocation * Heterogeneous Agents
Introduction to RoboCup
The RoboCup Federation
RoboCupSoccerSimulation LeagueSmall Robot League (F-180)Middle Size Robot League (F-2000)Sony Legged Robot League (Sponsoredby Sony)Humanoid League (From 2002)
RoboCupRescueRescue Simulation LeagueRescue Robot League
RoboCupJuniorSoccerRescueDance
Introduction to RoboCup
Simulation League
Soccer SimulatorTool for multi-agent systemsEnables two teams of 11 simulated autonomous roboticplayers to play soccerTwo coachesPhysics (e.g. stamina, recovery,weight, speed, wind, etc.)Sensors (e.g. see, hear)Time: 6000 cycles, eachcycle has 100ms (�10 min)
Introduction to RoboCup
Small Robot League (F-180)
Five robots, each having a foot print of at most 180 cm²Field: green carpet within a wooden enclosureSize: ping-pong table (274 cm x 152,5 cm)Ball: golf ballTime: 20 minutes, divided in two equal halvesNo camera on board
Introduction to RoboCup
Full video Cornell Big Red vs. Lucky Star Singapore
Middle Size Robot League (F-2000)Four robots, each having a foot print of at most 2025 cm²Field: 9x5m, greenBall: FIFA size 5Time: 20 minutes, divided in two equal halvesOn-board camera & other sensors (e.g. ultrasound,laser,…)
Introduction to RoboCup
Full video CS-Freiburg vs. Trackies, 2001
Sony Legged Robot LeagueFour robotsField: 3,5x2m, green, landmarksTime: twenty minutes, divided in two equal halvesRobot has 20 degrees of freedom7 touch sensors, on-board camera
Introduction to RoboCup
Full video
Humanoid LeagueWalking using two legs, no wheel, approximate body, consists of twolegs, two arms, one body, and one headHeightmax 40, 80, 120cmFields (6*Hmax, 9*Hmax)Solo games (e.g. penalty shootout), games40! humanoids available (e.g. PINO, Asimo, P3)
Introduction to RoboCup
Problems in LeaguesSimulation League
Little co-operation, physics not real, communication via server, mostly reactive agents, mostlyoffline-learning
F-180No camera on-board, no co-operation, no communication, central control, autonomy, mechanicalproblems, no learning
F-2000Image processing in real time (no 25 fps), no learning, no co-operation, extreme mechanicalproblems
Sony Legged League(No) communication, movements, no co-operation, image processing in real time
Humanoid LeagueAll of the above, balance
Introduction to RoboCup
Questions to introduction?
Areas of Artificial Intelligence (AI)
Expert systemsNatural language
systems
Vision
Robotics
Automatic proofsystemsMachine
learning
Automaticprogramming
Applications incognitive
psychology
Logics
LinguisticsPsychology
Optics
Pattern recognition
Mechanicalengineering
Logics
Programverification
Systemprogramming
Linguistics
AppliedSciences
Artificial Intelligence
Methods of AI
Heuristicsearch
Neural networks
Problem
Solving;
Reasoning
AI languages and
systems
Biology
Neurology
Physiology
Logics
Theory ofprogramminglanguages
Parallel systems
Combinatorics
Graph theoryKnowledge
representation
Epistemology
Psychology
Artificial Intelligence
What is Artificial Intelligence (AI)?
“[The automation of] activities that weassociate with human thinking, activitiessuch as decision-making, problem solving,learning…'' (Bellman, 1978)
“The study of mental faculties through theuse of computational models.''(Charniak+McDermott, 1985)
“The study of how to make computers dothings at which, at the moment, people arebetter.''(Rich+Knight, 1991)
“The branch of computer science that isconcerned with the automation of intelligentbehavior.''(Luger+Stubblefield, 1993)
• Systems that think like humans• Systems that act like humans
• Systems that think rationally• Systems that act rationally
Source: Russell/Norvig, 1995
„The study of the computations that make it possible to perceive, reason, and act.“(Winston, 1992)
Artificial Intelligence
Standard problems:Chess vs. RoboCup
Chess RoboCupEnvironment Static DynamicState change Turn taking Real timeInformation accessibility Complete IncompleteSensor readings Symbolic Non-symbolicControl Central Distributed
Artificial Intelligence
Artificial Intelligence & RoboCup:Research topics
SearchPlanningLearningSpatial reasoning
Temporal reasoningDecision makingCommunicationPerception
Artificial Intelligence
Search & Planning
Used methodsA*-search for positioning,search over plansAdaptive path planning(SSL), Uni AucklandHybrid navigation planning(MSL, AGILO), chooses thebest planning algorithm
ShortcomingsSearch spaces too big!bd memory, bd time(example)States for planningalgorithms not easy todescribeLocal minima
Robotic Soccer and AI
Learning
Used methodsReinforcement learning forsoccer keep away (SL),AT&T, USAReinforcement learning foreverything (SL), UniKarlsruhe, GermanyLearning strategy ofopponent (SL), Uni Bremen,Germany, Uni Osaka, JapanK-nearest neighbor inpursuit-evasion situations(MSL), Uni Auckland, NZ
ShortcomingsLearning takes a long time(training phase)Online learning due to realtime processing almost notfeasible (only smallproblems)
Robotic Soccer and AI
Spatial and temporal reasoning
Used methodsObject tracking during thegame, Uni Bremen,GermanyReasoning with partonomies(Schlieder et al. 2001)
ShortcomingsModelling effort highReasoner not available
Robotic Soccer and AI
(Dynamic) Decision making
Used methodsState charts and utilityfunctions for rational agents,(SL), Uni Koblenz, GermanyRobot navigationconsidering observationalcost (Sony), Uni Tokyo,JapanHorn clauses logic fordecisions (SL), Uni Koblenz,Germany
ShortcomingsDecision making dependson value of informationDynamic Decision Networksretain forward searchthrough concrete states(typical for search alg.)Horn clauses expressivity
Robotic Soccer and AI
Communication
Used methodsRadio communication (SSL)Acoustics (Sony), UNSW,AustraliaCo-operation based behaviordesign (SSL), Nanyang Uni,SingaporeFormal languages (SL), Coachlanguage, various teamsCo-operation to complete worldmodel (MSL), CS Freiburg,Germany
ShortcomingsSony League: acusticcommunicationOverlapping frequencies„Meaning“ of packets
Robotic Soccer and AI
Perception
Used methodsCooperative sensing (globalview), CS-Freiburg, GermanyFast image processing (realtime) with the concept ofperspective view, SharifUniversity, IranImage processing tools, UniBremen, GermanyMulti-sensor navigation,SocRob, PortugalOmni directional vision forgoalkeeper, Padua,Italy
Shortcomings„Ghosts“ (also communication)
Image processing
Robotic Soccer and AI
Remarks
Where can AI methods help?PlanningSearchingLearningDecision makingSpatial reasoningTemporal reasoningComputational logicMotor control(Multi-agent systems)
Where can‘t they help?MechanicsElectronicsPerceiving (sensor fusion)Image processing
and:“Emotional reasoning”
Questions?
Simulation league
Small size league (2000 game)
Middle size league
Sony legged league
2001 finals
Example 1: Identification of situations
Behavior of goalkeeper in SLTime series based decision treeinductionCoach collects data for time seriesQualitative abstraction of keybehaviorsApproximation of time series bypartially linear functionsExtracting rules from decision-tree
Robotic Soccer and AI
ResultsFirst results shortly after 500 cyclesMethod recognizes values nearly asimplemented in our goalieQuality increases with longer timeseriesScenario: FC Portugal vs. VirtualWerderAnalyzing our own goalieDecision tree is generated every 500cycles
if DistGoalGoalie < 3.65 and DistBallGoal < 19.36 and DistBallGoalie < 13.73then 1 (0.972222)
(...)
if DistGoalGoalie > 3.65 and DistBallGoalie < 13.73then 1 (0.972222)
(...)
if DistGoalGoalie > 3.65 and DistBallGoalie > 13.73 and TeamMemInPenArea < 1 and DistBallGoal > 19.44then 0 (0.75)
class 1Goalie leaves goal probability
class 0 Goalie doesn't move
Example 1: Identification of situations (2)
Robotic Soccer and AI
back
Goal: Track objects and their spatial and temporal relationsover time and interpret them during the game
Spatial Relation Intervals Player/Ball location
distance
SE
E
NE
N
NW
W
SW
S
S
meets
med
far
close
210 220 240
player meetsor close to
the ball
playerapproaching
the ball
ball fromviewpoint ofplayer indirection toopposite goal
N
EW
S
time/cycles
Example 2: Identification of situations
Robotic Soccer and AI
Fig.1: Two players fighting for the ball
Object Motion Intervals Playerspeed
direction
SE
E
NE
N
NW
W
SW
S
S
210 220 240
Movement in SE/E direction,i.e. to the opposite goal
none
med
fast
slow
time/cycles
Example 2: Identification of situations (2)
Robotic Soccer and AI
Object Motion Intervals Ball
time/cycles
speed
direction
SE
E
NE
N
NW
W
SW
S
S
none
med
fast
slow
Movement in SE/Edirection, i.e. to theopposite goal
210 220 240
Example 2: Identification of situations (3)
Robotic Soccer and AI
se4=meets(p2,b)se2=approaching(p2,b)
se3=departing(b,p1)
Building complex situations from simple events:Player 1 passes the ball to player 2 ...
Temporal relations between time intervals:meets(se1,se2) AND equal(se2, se3) AND meets(se3, se4)
se1=meets(p1,b)
time
Simple event approaching: 1 spatial, 2 motion intervals:
Constraint:
[ ][ ][ ]111 ,1
,
,1
ppp
bbb
vdirpmotion
vdirbmotion
distlocpspatial
=
=
=
( ) 11 0:,1 pp dirlocvbpgapproachin =∧>
Fig.1: Two players fighting for the ball
Example 2: Identification of situations (4)
Robotic Soccer and AI
Fig.1: Two players fighting for the ball
(a) Cycle 216 (b) Cycle 221 (c) Cycle 225
(d) Cycle 237 (e) Cycle 257 (f) Cycle 261
(g) Cycle 265 (h) Cycle 270 (i) Cycle 273
ResultsScenario:
60 cycles in a 4 vs 4 environmentStarting at fig. 1 in the high lighted section
Player team 1 and team 2 are in relation. This is for the ball (fig. 1).Ball is from both players (fig. 2a).A second player from team 1 approaches the ball (fig. 2b).The player reaches the ball ( ) (fig. 2c).The player and the ball move in direction of opposite goal ( or ) (fig. 2d-f).Players of team 2 are moving backwards. Building a defense position (fig. 2).Defenders of team 2 approaching the player with the ball until one of them is meeting him(again for the ball) (fig. 2e-g).Player of team 2 is still in relation to the ball whereas the player of team 1 is
to the ball but no more it, i.e. has lost it (fig. 2h).Ball is from player of team 2, i.e. he passes the ball (fig. 2i).
Interpretation of spatio-temporal relations:meets fighting
departing
meetsmeets close
fightingmeets
close meetsdeparting
Example 2: Identification of situations (5)
Robotic Soccer and AI
back
Back to communicationBack to perception
Robotic Soccer and AI Robotic Soccer and AI
Hmax = 40, 80, 120cm
Robotic Soccer and AI
Penalty shootout Heuristics
Example 8-puzzleBranching factor = 3, typicalsolution after 20 stepsExhaustive search: 320 statesWith repeated states only 9! =362880
h1(n) = total number of misplaced tilesh2(n) = total Manhattan distance, i.e.,number of squares from desired locationof each tile
5 4
16
7 3 2
8
1 2
48
7 6 5
3
start state
h1(n)=7
h2(n) = 18
goal state
back
Robotic Soccer and AI
RMIT SocBot Play system recognition using artificial neuralnetworks
Online coach (total view)Bounding boxGrid of 64 cells (binary inputs)If players per cell � 1 then cellpositive, negative otherwiseLeads to input vector for ANN16 play systems known (e.g.Catenaccio)612 training sets, 68 test (�10%)
Play system recognition using artificial neuralnetworks (2)
Results:Virtual Werder vs. CMUnited 99Virtual Werder vs. Mainz Rolling Brains
Performance against CMU better withdefensive strategyPerformance against MRB better withoffensive strategy
Total: performance better if opponentsplay system is known!
Mainz RB CMU-99
Def
.5-4
-1
0.5:0.9 0.1:9
Off.
3-4-
3
3.1:0.7 0:14Virt
ualW
erde
r
back
AIBO sensors
Source: UNSW technical report
AIBO viewCCD Camera176 x 144 pixelOne of 8 colors per pixel256 x 256 x 256 YUV colorspace (� 16 MB!)Only 6 bits used (� 512KB!)Image Run-lenghtencoded (RLE)
High resolution digitized image Medium resolution digitized image
High resolution color image Medium resolution color imagePictures: UNSW technical report back
What are partonomies?
PartonomiesSP-PART-OF
Spatial reasoning
?
TaxonomiesIS-A
Terminological reasoning
?Description logic
DL theorem prover
Introduction
Intentional behaviorWhat activity is theuser/player currentlyengaged in?What is she/he intendingto do next?
Example: location-basedservices
Inferring the intentions ofthe user from the user´slocation
Queuing: Simple behavior
Motion patterns
Change of locationPath s(t)Speed v(t)Acceleration a(t)?
Location-based ServicesInferring intentions mustbe based on motionpatterns in this case Skiing: Complex
behavior
Spatial partonomies
Geographic SpaceHierarchical decompositioninto administrative orfunctional unitsPartonomies encode spatial-part-of relations (DAG, ...)
Research resultsAI: spatial reasoninge.g. spatio-temporal changeCognition: mental maps
exhibit
room
wing
museum
Qualitative abstraction
Qualitative parametersposition: {inside,outside}distance: {any}duration: [second]
Spatial reasoningRelational algebras(Tarski)Computational propertiesare defined bycomposition tables, ...
inside outside
inside
outside
inside outside
any any
ABC
Relational composition
Qualitative abstraction
Qualitative parametersposition: {inside,outside}distance: {any}duration: [second]
Spatial reasoningRelational algebras(Tarski)Computational propertiesare defined bycomposition tables, ...
inside outside
inside
outside
inside outside
any any
ABC
Relational composition
Strategy diagnosis
German Research CouncilProject
„Automatic diagnosis ofstrategies of opponentrobots in a co-operativeenvironment“
Dynamic partonomiesAnalyze motion in thecontext of partonomies!Partonomies defined bysoccer players
back
Reinforcement learning
4 vs. 3 KeepawayRL as framework forsequential decisionproblems
RL advantagesIncludes stochasticDelayed rewardsArchitecture for big statespaces and many actionsRandom delays betweenactions possible
Players skills:HoldBall()PassBall(f)GoToBall()GetOpen
Source: Stone & Sutton (2002)
Reinforcement learning
back Source: Stone & Sutton (2002)
Defendersalways GoToBall()
13 variables, e.g.Distances between objects
Dist(F1,F2), dist(F3,C)
MinimaMinimum(dist(F1,F2))
AnglesAng(F1,F2,D1)
Q-learning for forwardsResultsErgebnisse
Beispiele:RandomHand-codedLearned
Path planning
Source: Baltes & Hildreth (2001)
Adaptive planning algorithmfor car-like mobile robotsIdea:
Keep old plan as long aspossibleCreate a new plan byadapting the old plan to thenew situation
Representation
Path planning
back Source: Baltes & Hildreth (2001)
Repair strategiesPositional adjustment
Start & end position
Shape adjustmentLength and curvature of segment
Type adjustmentSign of curvature
Segment structure adjustmentInsertion, breaks, etc. of segments
Plan justification adjustmentRemoves unnecessary plansegments
EvaluationSignificantly faster