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Updated: May 13, 2026

The HoneyComb Paradigm for Research on Collective Human Behavior

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

Energy parity games.

Krishnendu Chatterjee¹, Laurent Doyen

¹IST Austria (Institute of Science and Technology Austria), Austria.

Theoretical Computer Science

|March 9, 2013

Summary

This summary is machine-generated.

Winning strategies in energy parity games require exponential memory. The complexity of solving these games is NP ∩ coNP, with algorithms provided by reduction to energy games.

Keywords:

Games on graphs Parity objectives Quantitative objectives

Related Experiment Videos

Last Updated: May 13, 2026

The HoneyComb Paradigm for Research on Collective Human Behavior

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

Area of Science:

Theoretical Computer Science
Game Theory
Formal Verification

Background:

Energy parity games are infinite, two-player, turn-based games on weighted graphs.
Objectives combine qualitative parity conditions with quantitative energy (sum of weights) constraints.
These games serve as a simplified model for resource-constrained omega-regular specifications.

Purpose of the Study:

To analyze winning strategies and computational complexity of energy parity games.
To develop algorithms for solving energy parity games and related game types.
To explore the relationship between energy parity games and mean-payoff parity games.

Main Methods:

Analysis of winning strategies in energy parity games, considering memory requirements.
Complexity analysis using standard complexity classes (NP, coNP).
Algorithm development via reduction of energy parity games to energy games.
Logspace equivalence established between energy parity games and mean-payoff parity games.

Main Results:

Exponential memory is sufficient and potentially necessary for winning strategies.
The decision problem for energy parity games is in NP ∩ coNP.
An algorithm is presented for solving energy parity games by reduction.
The problem is logspace-equivalent to solving mean-payoff parity games.

Conclusions:

Energy parity games present a tractable model for combined qualitative and quantitative objectives.
The established complexity and algorithmic solutions contribute to the understanding of infinite games.
The findings offer a conceptually simple algorithm for mean-payoff parity games as a consequence.