Natural Cognition and Artificial Intelligence: What can biologists learn from AI?

logo by Jolyon Troscianko

Dr Jackie Chappell

Cognitive Adaptations Research GroupSchool of BiosciencesUniversity of Birmingham

What problem are we interested in?

Photo: Milwaukee County Zoo

Photo: Honda

cognitive mechanisms unclear

cognitive mechanisms well

understood

behaviour rich and fairly well understood

behaviour simple and usually optimised for particular

environment/context

Biomimetic robots as tools for probing animal behaviour

45°

Honey bee waggle dance

Taken from: Tanner & Visscher 2010

‘RoboBee’

- angle of waggle run- length of waggle run- vibrations and

changes in air currents- thoracic temperature

increase- pheromones

Which of these are decoded by watching bees?

Photo: http://robobee.eu

Robots to test hypotheses about mechanisms

Phonotaxis in crickets (Gryllus spp.)

♂

♀Photo: sanmartin@flickr

Photo: elchip@flickr From: physiology.org

The existing models

turn left turn right

left ear right ear

turn left turn right

if left > right

AND

AND

recogniser

if right > left

left ear right ear

recogniser recogniser

if left > right if right > left

Adapted from: Webb & Scutt (2000) Biol Cybern 82, 247-269.

The robot model

• Is the turn determined by the firing rate of auditory neurons on each side or a comparison of the time taken for sound to reach each ear?

• How is the song (characteristic repetition rate of syllables) recognised?

• Robot uses biologically-plausible 4 neuron system

• No mechanism for comparing songs or recognition

• Processes sound fast enough to use real cricket song as a stimulus

• Result: phonotaxis just like a cricket

Taken from: Webb & Scutt (2000) Biol Cybern 82, 247-269.

How do rats use their whiskers to explore?

Grant et al. (2009) J Neurophysiol 101, 862-874.

exploring floor exploring wall

min. spread perpendicular to

surface

min. spread perpendicular to

surface

max. spread parallel to surface

max. spread parallel to

surface

Using AI as a tool for thinking about cognition

Can orangutans plan their actions?

L M MS

gaps

closedend

openend

forward-facingtrap

backward-facingtrap

Tecwyn, Thorpe and Chappell (2012) Animal Cognition 15: 121-133

Amos

Puzzle tube

Tecwyn, Thorpe and Chappell (2012) Animal Cognition 15: 121-133

Puzzle tube

Tecwyn, Thorpe and Chappell (2012) Animal Cognition 15: 121-133

Using AI to approach the problem differently

problem to be posed to

animals in experiment

decompose problem using AI

techniques

compare results

simulate results using AI

techniques

refine problem

pose problem to test animals

finalised problem

problem to be posed to

animals in experiment

decompose problem using AI

techniques

simulate results using AI

techniques

refine problem

MAPL/MAPSIM planner

Collect resultsRun planner (MAPSIM)

Define individual problems

Decompose task domain

facts

facts

facts

states

preconditions

effects

actions

DOMAIN MODEL

goal state

initial state

facts known by agent

PROBLEM DESCRIPTION

(ONE OF MANY)

Generate plans

Simulate execution of

plans

OUTCOME: smallest

number of actions

necessary to attain goal state

effects of actions change the state of the world and preconditions for further action

1

2

3 4

Chappell & Hawes (2012) Philos Trans R Soc Lond B Biol Sci 367, 2723-2732.

How does this approach help?

• Work out:

• What problems the environment poses

• What information environment provides

• How animals access (some of ) this information

• How animals might solve these problems

• Then in a better position to hypothesise about mechanisms which would allow animals to solve problems in a particular way

What have we learned?

• Using robots/AI can be useful, but you have to be clear about what you gain

• Can be difficult to find projects where the problems are equally interesting to biologists and computer scientists

• Modelling behaviour in a robot can lead to important insights, but does not prove that a particular mechanism is used in animals

• Perception and action are relatively easy to model/produce in robots, but cognition is really hard

• Need to use AI to help define what the problem is first