Creative Invention Benchmark

Guzdial, Matthew; Liao, Nicholas; Shah, Vishwa; Riedl, Mark O.

doi:10.48550/arxiv.1805.03720

Cited by 1 publication

(1 citation statement)

References 7 publications

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“…Kralik et al (2016), compare the performance of their Hierarchical RL system to empirical data generated from trials of CPS in Rhesus monkeys. Guzdial et al (2018), propose a cross domain metric for evaluating the ability of a CPS system to obtain a goal state, where given a specific domain, an "Uncreative Max" (UM) baseline is developed to represent a solution most similar to the desired goal state, given CPS was not employed. Creativity is then measured as any positive score differential between a creative agent and its corresponding UM score.…”

Section: Benchmarkmentioning

confidence: 99%

Creative Problem Solving in Artificially Intelligent Agents: A Survey and Framework

Gizzi¹,

Nair²,

Chernova³

et al. 2022

jair

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Creative Problem Solving (CPS) is a sub-area within Artificial Intelligence (AI) that focuses on methods for solving off-nominal, or anomalous problems in autonomous systems. Despite many advancements in planning and learning, resolving novel problems or adapting existing knowledge to a new context, especially in cases where the environment may change in unpredictable ways post deployment, remains a limiting factor in the safe and useful integration of intelligent systems. The emergence of increasingly autonomous systems dictates the necessity for AI agents to deal with environmental uncertainty through creativity. To stimulate further research in CPS, we present a definition and a framework of CPS, which we adopt to categorize existing AI methods in this field. Our framework consists of four main components of a CPS problem, namely, 1) problem formulation, 2) knowledge representation, 3) method of knowledge manipulation, and 4) method of evaluation. We conclude our survey with open research questions, and suggested directions for the future.

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Section: Benchmarkmentioning

confidence: 99%