Brussels 1

WP 1 Environment and Challenges

Stephan SchusterUniversity of Surrey

Review Meeting Brussels, 31/10/2006

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WP 1 Objectives

Build software environment Define and formalise suitable

challenges Compare solutions found by

agents to solutions found by human societies

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WP 1 Status

Year 1 Year 2 Year 3

Environment Software first version

First challenge implementations and final EM (D6)

Comparison with human societies

Challenge implementation (ongoing)

Challenge implementation (ongoing)

Challenge definition (M1.1)

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Environment overview

Specifies the composition of the world (plants, tokens etc.) and physical constraints of agents (e.g. agent lifespan, metabolism, mating rules) for a scenario

Provides the actions agents can apply in this world (subject to the physical and logical constraints set out in the spec)

Scenarios are generated from a scenario definition file

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Physics of the environment Environment

Maximum number of agents (MaxPop) and plants (MaxPlants) Time steps per virtual day (T)

Agents Maximum viewing (N) and hearing (H) distance Maturity age (AI), Maximum age (MaxAge) Pregnancy period (Mc), Energy share to child (Ms)

Plants Energy increase

per time step (Er) Time steps to grow (Tr)

MAX

06 months 12 months 18 months

AVERAGE PLANT ENERGY

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Challenges - Definition

A challenge is a specific configuration of the environment. A challenge consists of Specification of a problem environment Translation of the problem into

environmental constraints and distribution of objects

Possible criteria for success

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Implementing/Formalising Challenges

The challenge is to be defined by specifying mainly the geographical and physical features of the world

Most scenarios are simple – based on food gathering (e.g. interim challenges, or Hunter-Gatherer Challenge from the Annex)

So: The scenarios are described and generated by the spatial distribution of food

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Implementing/Formalising Challenges – ‘Scenario Files’

Modeller defines the location and distribution of objects (e.g. 100 agents with preferences for plant type x in region (0 0, 0 100, 100 100, 100 0)

Detailed setup possible, but may result in repetitive information and large files

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Implementing/Formalising Challenges – ‘Scenario Generator’

Idea: Describe the world at an abstract level, let the software generate objects and geography matching the description

For example: Uneven distributed patches of plants, long distances to travel, agents with different preferences …

NTSG generates a world satisfying such kinds of constraints

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Software

Architecture + implementation

Agents

EM

NTVM

Platform

Postgres MapViewer

NTSG

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Tools - NTSG

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Tools - MapViewer

Visualisation Allows scrolling and zooming the whole

landscape Uses Open-Source GIS software and a

spatial postgres database Thin client accessible from any

browser

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Tools – MapViewer

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Summary

EM released First challenges implemented Software: NTSG, MapViewer