Emergence of Collective Behaviors in Hub-Based Colonies using Grammatical Evolution

Aadesh Neupane

Problem

- Design collective behaviors is cumbersome

- Requires experts for modeling

Solution

- Evolutionary computation to generate collective behaviors

$$\frac{dR}{dt} = -aR + vnD , R(0) = R_0$$

$$\frac{dO}{dt} = aR -bO +cE, O(0) = O_0$$

$$\frac{dE}{dt} = q(D)bO - cE, E(0) = E_0$$

$$\frac{dA}{dt} = p(D)bO - mA + wnD$$

$$\frac{dD}{dt} = mA - nD, D(0) = D_0$$

Reference: Stability of choice in the honey bee nest-site selection process

Presentation Structure

  • Thesis Statement
  • Related Work
    • Evolutionary Robotics
      • Neural Nets
      • Distributed Evolution
    • Grammatical Evolution
      • Genotype
      • Santa Fe Trail
      • Mapping
  • GEESE
  • Validation
    • Effectiveness
    • Robustness
    • Breadth

Thesis Statement

The interaction of hundreds of agents within the framework of distributed grammatical evolution will increase the effectiveness of evolving collective behaviors of bio-swarms. The evolved behaviors can be reused for different collective problems that have similar properties. Furthermore, with a slight variation of the objective function, the same set of primitive behaviors, encoded in the grammar, can lead to collective behaviors over a wider range of collective problems.

Related Work - Evolutionary Robotics

Reference: Evolution of collective behaviors for a real swarm of aquatic surface robots

  • Swarm behaviors like homing, dispersion, clustering and monitoring by Duarte et. at
  • Validation of scalability, flexibility, and robustness on transferred controller

Neural Network based controllers

Merits

  • Easy mapping from sensory inputs into actuators values
  • Provides generalized solutions
  • Only a few human inputs needed

Demerits

  • Issues with reverse engineering
  • Insights on collective behavior difficult
  • Almost impossible to modify the behaviors

Related Work - Evolutionary Robotics

Reference: Evolution of collective behaviors for a real swarm of aquatic surface robots

Neural Network based controllers

Related Work - Evolutionary Robotics

Reference: GESwarm: Grammatical Evolution for the Automatic Synthesis of Collective Behaviors in Swarm Robotics

  • Foraging problem using GE by Ferrante et. al.
  • Preconditions, low-level behavior and actions gave behaviors

Grammatical Evolution (GE) based controllers

Advantages

Disadvantages

  • BNF grammar and objective function
  • Analyzing and modifying collective behaviors easier
  • Primitive low-level rules defined by experts
  • Mapping to raw sensors and actuators values difficult

Related Work - Distributed Evolution

Reference: odNEAT: An algorithm for distributed online, onboard evolution of robot behaviours

odNEAT

  • Online distributed evolution of Neural networks
  • Applicable for online learning in groups of embodied agents (robots)
  • Performs well in aggregation task

Combination of best features from all these works?

- An online distributed Grammatical Evolution

Related Work - Grammatical Evolution

BNF Grammar

Population of Genome

Corresponding Phenotype

Grammatical evolution: Evolving programs for an arbitrary language

Grammatical Evolution

BNF Grammar

<code>          ::=  <code> | <progs>
<progs>        ::=  <condition> | <prog2> | <prog3> | <op>

<condition>  ::=  if_food_ahead(<progs>, <progs>)
<prog2>        ::=  prog2(<progs>, <progs>)
<prog3>        ::=  prog3(<progs>, <progs>, <progs>)
<op>              ::=  left | right | move

- Santa Fe Trail

- Represented by tuple \(N, T, P, S\)

  • N -> Set of all non-terminals
  • T  -> Set of all terminals
  • P -> Set of productions that map \(N\) to \(T\)
  • S -> Initial start symbol

Grammatical Evolution

Santa Fe Trail

  • Objective
    • Find all food using maximum of 600 moves
  • 32 x 32 cells
  • Optimal trail
    • 144 cells
    • 89 food
    • 55 gaps
  • Actions :
    • Left
    • Right
    • Forward

Grammatical Evolution

Genome/ Genotype / Individual

  • Defines the proceedings of left-derivation
  • \({Codon}\) is a group of symbols, usually 4 or 8.

Grammatical Evolution - BNF Grammar

Mapping

  • Let \(c\) be codon integer
  • \(A\) denotes the left-most non-terminal in the derivation
  • \(r_A\) denotes the number of right-hand side rules associated with the production of \(A\)
  • RHSRule = \(c\%r_A\)

Phenotype

  • The output from the mapping process is the phenotype
  • The phenotype represents a valid expansion of the BNF grammar
  • \(if\_food\_ahead(move, left)\)

Example of Grammatical Evolution

RHSRule = \(c\%r_A\)

<code>          ::=  <code> | <progs>

<progs>        ::=  <condition> | <prog2> | <prog3> | <op>

<condition>  ::=  if_food_ahead(<progs>, <progs>)

<prog2>        ::=  prog2(<progs>, <progs>)

<prog3>        ::=  prog3(<progs>, <progs>, <progs>)

<op>              ::=  left | right | move

Grammatical Evolution

 GE Pipeline

GEESE

GEESE

Hello! Neighbour!

How are you doing with the phenotype in this environment?

Hello!

I collected 35 oz of water with the phenotype.

Take my genome and perform magic using genetic operators !

GEESE Pipeline

Swarm Behaviors

  • Primitive Behaviors
    • Function that maps the set of sensor values to actuator commands
    • Behaviors  as state-machine or behavior trees
  • Examples
    • GO_TO_HUB
    • FOLLOW_LIGHT
    • SEND_SIGNAL

Thesis Statement

The interaction of hundreds of agents within the framework of distributed grammatical evolution will increase the effectiveness of evolving collective behaviors of bio-swarms. The evolved behaviors can be reused for different collective problems that have similar properties. Furthermore, with a slight variation of the objective function, the same set of primitive behaviors, encoded in the grammar, can lead to collective behaviors over a wider range of collective problems.

Validation

Claim Metric
Effectiveness Quality solutions in fewer generations
Robustness Same behavior for both single and multiple foraging problem
Breadth
Solutions for Foraging and cooperative transport problem

Validation

Effectiveness

Validation

Robustness

Evolved BT

Single Source Foraging problem

Multiple Source Foraging problem

Validation

Breadth

Swarm BNF Grammar

Foraging problem

Cooperative Transport problem

Acknowledgement

This work has been funded by ONR grant number N000141613025.

Thank You!

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