Lecture � Curious machines

Greg Detre

Wednesday, May 07, 2003

 

Gallistel, �The principle of adaptive specialisation as it applies to learning and memory�, in Kluwe, Luer and Roesler, �Principles of human learning and memory�

adaptive specialisation in the memory mechanism

thermodynamic stability

high density

information is information

coding

adaptive specialisation in the learning mechanism

learning current location

learning the solar emphemeris

language

conditioning

 

Things to think about

Question to think about: Is curiosity a "thing", thatis, an adaptive specialization, or a collection of mechanisms that together produce what we call curiosity?

 

  1. define curiosity

exploration vs exploitation tradeoff

temperature/stochastic

it�s the filter that tells you what concepts are interesting, or how to categorise your experience

learning about your environment

looking for a bunch of good cases/exemplars on which to rest your understanding, and which seem to be representative of the space � too specific to CBR

a methodology??? a drive??? � see lecture 030205

  1. is it a module or a set of qualities?

it�s got to be a set of qualities � what would a curiosity module look like???

well, unless maybe you�re in a safe niche and don�t need to be curious, except every so often

in which case it could maybe be something that gets turned on/off at need, kind of like a high-level model in a behaviour-based system (eating, sleeping, playing, followingMother, beingCurious etc.)

it could just be a parameter that affects the rest of the system

  1. what are 3 things you would need to implement in order to build a curious machine/module/whatever?

a means of evaluating how happy you are with current knowledge, i.e. whether you can survive on it if things stay the same, whether it�s barely adequate

a means of testing how much more there is to learn, or how fast the state of the world is changing, or whether you�re in a reasonably global optimum

a system for changing your current cognitive mechanisms/behaviour correspondingly, either by adding randomness, a bent for exploration, the interactions between them etc.

 

Define curiosity

Emily: using previous knowledge to gain insight into new information � that�s just learning

me: exploit vs explore

Hugo: suitcase of heuristic mechanisms for principled state-space discovery + learning

Andrea: actively seeking to understand/explain new experiences in the world

Derek: proactively exploring one�s environment in order to explore new strategies to maximise fitness � related to desire to play

Chris: behaviour that resolves uncertainties re novel circumstances, objects + phenomena

ability/desire to explore one�s space and extract features that enhance one�s understanding/functioning

desire to know something

Daphna: eager to learn more/ active desire to learn/know, interested in people + things around you

being proactive to learn about objects around you

Jessie: opportunistically seeking out information not required for current task

Cynthia: motivation to learn what it does not already understand and ability to act successfully on it

Deb: hunger [i.e. a basic disposition] for knowledge that matters [of (evolutionary) significance to the organism]

Bruce: proactive goal-directed exploration with potentially distal integration and consequences

 

basically, it�s all just about going out and learning important stuff

 

being curious:

learning component

exploration

what �matters�, i.e. relevant to you eventually

interesting/novel/unexpected

drive/proactive

knowledge may be most relevant down the road

 

Deb: �curiosity� is just the drive

 

Derek: hunger for knowledge that doesn�t appear to matter

me: it�s where there may be a proximate reward if you learn something, but failing to learn won�t have a proximate cost (�you wouldn�t say you�re curious about how to get your leg out of a beartrap�) (if you fail to learn it)

 

Deb: C S Peirce � hard vs soft, things that sense and brace yourself vs compliance properties of rocks and sponge, things that one can theorise about that will never have consequence (and can�t be verified)

 

Deb: what matters is knowledge that you can make use of in servicing your highest level goals � benefit

 

Deb: it doesn�t make sense to talk of learning new goals at the highest levels

 

Module vs set of qualities

Jessie: not a module, but there is an innate bias towards things that are interesting/matter evolutionarily

Daphna: not a module

Kai-Yuh: neurologically encoded

e.g. music, not evolutionarily significant

brainwide neural activity

Deb: i.e. it�s implemented

general pattern that might let you generate new types of curiosity

Chris: boils down to implementation, multiple types of curiosity

Derek: neither, but especially not a unitary drive

consequence of not functional, metamorphic, play

Andrea: not a module, emergent quality of underlying (active) learning mechanisms

Hugo: same

critical mass of diversity of interests and competing mechanisms

me: affective set of parameters

weak method (in the Norman sense)

Deb: neither, see question 3

Cynthia: Andrea + Hugo camp - situatedness

 

3 things you would need to implement

Kai-Yuh: brain, vat (bat), machine � and I want a beach

related to past experiences

Deb:

analysis of ambiguity � reflect on what you don�t know

planning mechanisms to reduce ambiguity � e.g. changing your perspective or asking a question to resolve the ambiguity

constrained causal analysis of observations (analyses of causality) � trying to understand why what happened happened

Hugo:

three steps:

have a rough model

predictive forward projection

focus on some aspect � agenda or attention mechanism

situated model refinement � update that model

expectation-violation is a trigger of curiosity

Deb: many types of curiosity, many types of triggers

Daphna:

whole cognitive architecture

ways to act on the world

beliefs + representations

Derek: mired too much in high-level cognitive representations � what about simple animals like cats

the real challenge is how to integrate results of opportunistic learning into continuing evolution of behaviour system � making best use of what you�ve discovered

Bruce: beavers � stick the stick into mud where the current is fastest

Deb: Griffin gave a counter-example of removing sticks sometimes from high-pressure areas

Jessie:

acquire + store information

drive to do that for seemingly unnecessary information

evolutionary bias towards being interested in things that turn out to be important

 

way to extract features from the environment

 

me:

a means of evaluating how happy you are with current knowledge, i.e. whether you can survive on it if things stay the same, whether it�s barely adequate

a means of testing how much more there is to learn, or how fast the state of the world is changing, or whether you�re in a reasonably global optimum

a system for changing your current cognitive mechanisms/behaviour correspondingly, either by adding randomness, a bent for exploration, the interactions between them etc.

OR me:

sense of what�s salient influencing your seeking-behaviour

whether you�re feeling confident/optimistic/inventive, i.e. safe + worth doing

curiosity as a (micro-???)stage like development, pruning, sleeping

 

Bruce:

trigger for when to be curious

exploration process

implicit mechanism to direct it � domain-dependent

when to stop being curious

integration phase

 

 

Bruce: reading a book recommended by Coppinger called �Individual development and evolution�

 

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