Emergence of Collective Behaviors in Hub-Based Colonies using

Grammatical Evolution

and

Behavior Trees

Aadesh Neupane

Problem

- Designing 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

  • Demo
  • Related work
  • GEESE
  • Challenges
  • Validation
  • Summary

Real Ants

Demo

Single Source Foraging

Demo

Cooperative Transport

Demo

Nest Maintenance

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: 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 - Behavior Tree

Reference: Behavior Trees for Evolutionary Robotics

DelFly Explorer

Related Work - 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?

- A multi-agent Grammatical Evolution with BT

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 !

Agent

Initialization

GEESE Pipeline

Simulator

BNF Grammar - Swarms

Swarm Behaviors

Composite Carry

Composite Drop

Move Towards

Explore

Challenges

Non-episodic learning

Partially observable

Huge search space

Credit Assignment

Solution - Fitness Functions

Task-Specific

  • Foraging                             :   Total food collection in hub
  • Cooperative Transport   :   Total heavy object collected in hub
  • Nest Maintenance            :   Total debris removed from hub

Spatial

  • Exploration                         :   Area explored
  • Prospective                         :   Total objects carried

Diversity

  • Phenotypic Diversity        :    Total unique behaviors

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

Effectiveness

Solves the Santa Fe Trail in 324 steps

GE [Novelty] solved in 331 steps

Validation

- Breadth

Cooperative Transport

Nest Maintenance

Cooperative Transport

Nest Maintanence

Interesting Foraging Behavior

Interesting Nest Maintenance behavior

Summary

Claims from thesis Evidence
Effectiveness Least number of steps for SantaFe Trail problem.
Robustness Transferability of evolved behaviors in foraging problems
Breadth Applicability of same BNF grammar for different swarm tasks
Novelty Combination of GE with BT

Future Work

  • General

    • Test in actual robots
    • Probabilistic modeling
  • Learning

    • Solve regression and classification  tasks
    • General consensus decision-making
  • Swarms

    • Interference effects
    • Analysis of behavioral diversity and diversity heuristic

Acknowledgement

This work has been funded by ONR grant number N000141613025.

Thank You!

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