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Updated: Jun 9, 2026

The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

A General Upper Bound for the Runtime of a Coevolutionary Algorithm on Impartial Combinatorial Games.

Alistair Benford1, Per Kristian Lehre2

  • 1School of Informatics, University of Edinburgh, Edinburgh, UK.

Algorithmica
|June 8, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces runtime analysis for coevolutionary algorithms (CoEAs) in combinatorial games. It establishes an upper bound for discovering optimal strategies, crucial for avoiding pathological behaviors in game AI development.

Keywords:
CoevolutionCombinatorial gamesRuntime analysis

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Last Updated: Jun 9, 2026

The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

Area of Science:

  • Artificial Intelligence
  • Game Theory
  • Computational Complexity

Background:

  • Combinatorial games present complex dynamics, serving as crucial test cases for game-playing agent training algorithms.
  • Coevolutionary algorithms (CoEAs) that train using self-play are powerful but susceptible to pathological behaviors like cycling, especially in games with intransitive payoff landscapes.
  • Runtime analysis offers insights into designing CoEAs to mitigate these issues.

Purpose of the Study:

  • To extend the scope of runtime analysis to coevolutionary algorithms applied to combinatorial games.
  • To provide a theoretical framework for understanding and improving CoEA performance in game playing.

Main Methods:

  • Developed a general upper bound for the number of simulated games required by a simple estimation of distribution algorithm.
  • The analysis focuses on discovering optimal strategies with high probability within impartial combinatorial games.
  • Applied the theoretical results to analyze well-known games such as Nim, Chomp, Silver Dollar, and Turning Turtles.

Main Results:

  • Proved a general upper bound for the runtime of a specific CoEA in discovering optimal strategies for impartial combinatorial games.
  • The derived bounds are polynomial or quasipolynomial for many games, offering practical implications for algorithm design.
  • This work represents the first runtime analysis of CoEAs specifically for combinatorial games.

Conclusions:

  • The findings provide critical theoretical insights into the behavior of CoEAs in combinatorial games.
  • This research is a foundational step towards a comprehensive theoretical framework for coevolutionary approaches in game AI.
  • The established bounds can guide the development of more robust and efficient game-playing agents.