intro to cogsci: embodiment 1

Symbolism & connectionism vs. EmbodimentIntelligence in actionEmbodied congition

Cognition and EnvironmentIntelligence without representation

Cognitive science paradigms: Embodiment

Kristína Rebrová

Kristína Rebrová Cognitive science paradigms: Embodiment



Contents

1 Symbolism & connectionism vs. Embodiment

2 Intelligence in action

3 Embodied congition

4 Cognition and Environment

5 Intelligence without representation




Physical symbol system hypothesis(Newell & Simon, 1976)

Physical symbol system is a necessary and sufficient conditionfor general intelligent action.Physical symbol system is a machine that produces throughtime an evolving collection of physical patterns called symbolstructures.Arbitrary links from symbolic code to sensori-motor referentsIntelligence occurs via reasoning (searching for operators;logical inference)Intelligence = computation - separate from sensors andeffectorsIntelligence in nature (animals might behave intelligently, butnot think so much as humans)




(more) Traditional approaches

Symbolismthought is a manipulation of abstract symbolimplies process of designing, and an external interpreter

Connectionismknowledge is encoded in weights and structure of the networkimplies learning, but what is learned also has to be interpreted

Problemsframe problemsymbol grounding problem




Embodiment

having a physical bodywhat does it mean for theagent’s cognition?what does it mean for acognitive scientist?PSSH type approachesemphasize thinking... whatabout intelligence based onacting?




William Grey Walter

neurophysiologist and robotician (February 19, 1910 – May 6, 1977)

Machina speculatrix (1948-49) robots Elmer and Elsie, tortoisescapable of phototaxis, by which they could find their way to arecharging station when they ran low on battery powerthe turtle’s complex physical behavior rests on:

simple circuit of two sensors, two vacuum tube "neurons" andsome RC components and two relaysrecursive mechanical feedback loop between the turtle motors,the physical environment (light and obstacles) and the circuit

analog processes to simulate brain processes(Turing, Von Neuman: digital processes)http://www.youtube.com/watch?v=lLULRlmXkKo


http://www.youtube.com/watch?v=lLULRlmXkKo



Braitenberg vehicles (1984)

sensors – light detectors + actuators –wheelsbehavior depends on connections betweensensors and wheelsstrive to achieve certain situations andavoid otherschange the course when the situationchangesexhibit a complex and dynamic behaviordependent on their “bodies”

http://www.youtube.com/watch?v=NJo5HEdq6y0




Intelligence by Mechanics

Passive WalkersMcGeer (1990)http://www.youtube.com/watch?v=CK8IFEGmiKYhttp://www.youtube.com/watch?v=N64KOQkbyiI

Theo Jansen - Kinetic Sculptorhttp://www.youtube.com/watch?v=WcR7U2tuNoYhttp://www.youtube.com/watch?v=WI90TgmjX4U




Intelligence by Material

Example: Octopus Projecthttp://www.youtube.com/watch?v=TW3XMPi_wng


http://www.youtube.com/watch?v=TW3XMPi_wng



Embodied cognition

an agent’s embodiment and situatedness is constitutive of itsperceiving, knowing and doingGroundedness: cognition grounded (anchored) in the physicalworld = embodied + embedded

embodied: agent has a body that provides direct sensationsand allows actionsembedded: situated in an environment that provides concreteexperienceboth body and environment constitute, enhance, but also limitagent’s intelligence

Situatedness: agent acquires knowledge about its environmentonly through sensors and in interaction with the world




Complete autonomous agents

Pfeifer & Scheier, 1999

“Fungus eaters” (Toda, 1982)

able to survive in complexenvironment

Completebehaving autonomously

Situatedview the world from theirperspective

Embodiedphysical agents, sensors, actuators

Self-sufficientgather food, make energyable to sustain itself over extended periods of timeKristína Rebrová Cognitive science paradigms: Embodiment



Complete autonomous agents

AutonomyNo control from the outside (from the designer)Increased by: self-sufficiency, situatedness, learning ordevelopment, and evolution

Action selection problembehavior control & designobserver based segmentation of behaviorno straightforward mapping of desired behaviors to internalactions




Adaptation

Evolutionarypeppered moth

Physiologicaladaptation to changes intemperature

Sensorychanges in the diameterof the pupil

By learning




Frame-of-reference problemfrom Pfeifer and Scheier (1999)

Perspective: observer vs. agentBehavior-vs-mechanism: behavior cannot be explained on thebasis of internal mechanisms onlyComplexity: the complexity we observe != complexity of theunderlying mechanisms




Ecological niche

The range of each environmental variable such as temperature,humidity, and food items, within which a species can existandreproduce (Wilson, 1975)

Environment must be characterized with respect to agent’scomplexityBut also complexity of the environment is a prerequisite for thecomplexity of an agent’s behavior




Umwelt (1984)

self-centered worldBiology should study organisms not as objects, but as activesubjects.Organisms can have different Umwelten, even though theyshare the same environmentUmwelt = subjective world of an organism

individual organism is always actively creating it’s individualUmwelt.is formed by perceptual and effector worlds togetherthis creative process is related to meanings determined by theanimal’s internal states, needs, design, etc.




Umwelt of a tick

Tick has 3 successive reflexes:Butyric acid as perceptual cue – tic let go and dropsTactile cue of hair – move aroundSkin’s heat – suck




Different visual Umwelten

(Uexküll, Brock 1927)




Wirkwelten (effect worlds) of a human, a dog and a fly

(Uexküll, Kriszat 1934: 56–58)




Intelligence without representation (Brooks, 1987)

Competences – layersadded incrementallySituated & embodied

the world is its own best modelno abstract representation needed

Inspiration from evolution. . . “mobility, acute vision and the ability to carry outsurvivalent tasks in dynamic environment provide a necessarybasis for development of true intelligence.”




Brooks’ creatures

Engineering methodologyCreatures

cope appropriately and timely with changes in environmentare robustmaintain multiple goalsdo something, have purpose in being

Decomposition by activity (pattern of interactions with theworld)Tested in the real world




Levels of activity

Run in parallel unaware of any other higher levels and competeExtract only relevant aspects of the worldLow-level activities

reactions to dangerous/important changes in environmentup-to-date idea about the world




Subsumption architecture

SuppressionNew layer is connected to the input of the existing layer andsuppresses new (incoming) messages

InhibitionNew layer is connected to output and inhibits outgoingmessages on the existing layer

Higher levels send information to lower levelsLevels are built incrementally and tested at each stepModular architecture (brain modalities similarity)




Problems of SA

How many layers can be built?How complex behaviors can be made without centralprocessing unit?Can higher-level functions (e.g. learning) occur in fixedtopology networks of simple finite state machines?




Representing the Body

Example: Starfish Self Modeling Robothttp://www.youtube.com/watch?v=ehno85yI-sAhttp://www.youtube.com/watch?v=msw267lisow




Forward and Iverse model

cognition is about anticipation and planningWolpert and Kawato (1998), Wolpert et al. (2003)forward: to generate predictions about the next state of theworldinverse: reversely activating actions that could possibly lead tothe observed situationwork together




The end

Thank you for your attention

[email protected]




Lets have fun...

army robot Big Dog:http://www.youtube.com/watch?v=W1czBcnX1Wwbuggy beta version:http://www.youtube.com/watch?v=VXJZVZFRFJc


intro to cogsci: embodiment 1

Education

embodiment intelligence

environment intelligence

representation intelligence

representation embodiment

eectors intelligence

agents embodiment

symbolism connectionism

agents cognition