Solving Pictorial Jigsaw Puzzle by Stigmergy-inspired Internet-based Human Collective Intelligence

TL;DR

This paper presents a stigmergy-inspired Internet-based collective intelligence approach for solving pictorial jigsaw puzzles collaboratively in cyberspace, demonstrating improved scalability and efficiency over face-to-face methods.

Contribution

It introduces the EIF loop framework for distributed puzzle solving, integrating exploration, integration, and feedback to enhance group performance in online settings.

Findings

Puzzle-solving time is nearly inversely proportional to the number of players.

Group collaboration reduces solving time by 31.36%-64.57%.

The approach outperforms face-to-face collaboration in scalability for groups of 10 players.

Abstract

The pictorial jigsaw (PJ) puzzle is a well-known leisure game for humans. Usually, a PJ puzzle game is played by one or several human players face-to-face in the physical space. In this paper, we focus on how to solve PJ puzzles in the cyberspace by a group of physically distributed human players. We propose an approach to solving PJ puzzle by stigmergy-inspired Internet-based human collective intelligence. The core of the approach is a continuously executing loop, named the EIF loop, which consists of three activities: exploration, integration, and feedback. In exploration, each player tries to solve the PJ puzzle alone, without direct interactions with other players. At any time, the result of a player's exploration is a partial solution to the PJ puzzle, and a set of rejected neighboring relation between pieces. The results of all players' exploration are integrated in real time through integration, with the output of a continuously updated collective opinion graph (COG). And through feedback, each player is provided with personalized feedback information based on the current COG and the player's exploration result, in order to accelerate his/her puzzle-solving process. Exploratory experiments show that: (1) supported by this approach, the time to solve PJ puzzle is nearly linear to the reciprocal of the number of players, and shows better scalability to puzzle size than that of face-to-face collaboration for 10-player groups; (2) for groups with 2 to 10 players, the puzzle-solving time decreases 31.36%-64.57% on average, compared with the best single players in the experiments.