Searching for surprise

Inspired by the notion of surprise for unconventional discovery in computational creativity, we introduce a general search algorithm we name surprise search. Surprise search is grounded in the divergent search paradigm and is fabricated within the principles of metaheuristic (evolutionary) search. The algorithm mimics the self-surprise cognitive process of creativity and equips computational creators with the ability to search for outcomes that deviate from the algorithm’s expected behavior. The predictive model of expected outcomes is based on historical trails of where the search has been and some local information about the search space. We showcase the basic steps of the algorithm via a problem solving (maze navigation) and a generative art task. What distinguishes surprise search from other forms of divergent search, such as the search for novelty, is its ability to diverge not from earlier and seen outcomes but rather from predicted and unseen points in the creative domain considered.This work has been supported in part by the FP7 Marie Curie CIG project AutoGameDesign (project no: 630665).peer-reviewe

A Computational Model of Surprise

The computation of surprise is one factor necessary for the computational detection of creativity in product design. An original computational model of surprise is proposed. A demonstration program was written to demonstrate the model\u27s validity. An experiment on human subjects was performed to test the program\u27s accuracy. The results of the experiment are analyzed, the model is assessed, and conclusions are made. This work is a promising start toward the goal of a practical computational surprise detector

Erato: Automatizing Poetry Evaluation

We present Erato, a framework designed to facilitate the automated evaluation of poetry, including that generated by poetry generation systems. Our framework employs a diverse set of features, and we offer a brief overview of Erato's capabilities and its potential for expansion. Using Erato, we compare and contrast human-authored poetry with automatically-generated poetry, demonstrating its effectiveness in identifying key differences. Our implementation code and software are freely available under the GNU GPLv3 license