User:Entropyboy7/Computational creativity

Original article: Computational creativity

Proposal: Added section.

Historical Evolution of Computational Creativity

The use computational processes to generate creative artifacts has been present from early times in history. During the late 1800’s, methods for composing music combinatorily were explored, involving prominent figures like Mozart, Bach, Haydn, and Kiernberger. [1] This approach extended to analytical endeavors as early as 1934, where simple mechanical models were built to explore mathematical problem solving [2]. Professional interest in the creative aspect of computation also was commonly addressed in early discussions of artificial intelligence. The 1956 Dartmouth Conference, listed creativity, invention, and discovery as key goals for artificial intelligence.[3]

As the development of computers allowed systems of greater complexity, the 1970’s and 1980’s saw invention of early systems that modelled creativity using symbolic or rule-based approaches. The field of creative storytelling investigated several such models. Meehan’s TALE-SPIN (1977) generated narratives through simulation of character goals and decision trees. Dehn’s AUTHOR (1981) approached generation by simulating an author’s process for crafting a story. [4] Beyond narrative generation, computational creativity expanded into artistic and scientific domains.

Artistic image generation was one of the disciplines that saw early potential in generated artifacts through computational creativity. One of the most prominent examples was Harold Cohen’s AARON, which produced art through composition and adaptation of figures based on a large set of symbolic rules and heuristics for visual composition. Some systems also tackled creativity in scientific endeavors. BACON was said to rediscover natural laws like Boyle’s Law and Kepler’s law through hypothesis testing in constrained spaces (Boden 2009).

By the 1990’s the modeling techniques became more adaptive, attempting to implement cognitive creative rules for generation. Turner’s MINSTREL (1993) introduced TRAMs (Transform Recall Adapt Methods) to simulate creative re-use of prior material for generative storytelling. Meanwhile, Pérez y Pérez’s MEXICA (1999) modeled the creative writing process using cycles of engagement and reflection (Gervás 2009). As systems increasingly incorporated models of internal evaluation, another approach that emerged was that of combining symbolic generation with domain-specific evaluation metrics, modeling generative and selective steps to creativity

In the field of generational humor, the JAPE system (1994) generated pun-based riddles using Prolog and WordNet, applying symbolic pattern-matching rules and a large lexical database (WordNet) to compose riddles involving wordplay [5]. WordNet is a system developed by George Miller and his team at Princeton, its platform and inspired word-mapping structures have been used as the backbone of several syntactic and semantic AI programs. A notable system for music generation was David Cope’s EMI (Experiments in Musical Intelligence) or Emmy, which was trained in the styles of artists like Bach, Beethoven, or Chopin and generated novel pieces in their style through pattern abstraction and recomposition (Boden 2009).

In the 2000s and beyond, machine learning began influencing creative system design. Researchers such as Mihalcea and Strapparava trained classifiers to distinguish humorous from non-humorous text, using stylistic and semantic features. (Ritchie 2009) Meanwhile custom computational approaches led to chess systems like Deep Blue generating quasi-creative gameplay strategies through search algorithms and parallel processing constrained by specific rules and patterns for evaluation.[6]

The institutional development of computational creativity grew along its technical advances. Dedicated workshops such as the IJWCC emerged in the 1990s, growing out of interdisciplinary conferences focused on AI and creativity. By the early 2000s, the field coalesced around annual conferences like the International Conference on Computational Creativity (ICCC). [7] Recently, with the advent of Deep Learning, Transformers, and further refinement in Machine Learning structures, computational creativity’s implementation space has expanded.

References:

1 “Ars Combinatoria: Chance and Choice in 18th Century Music”, Leonard Ratner in Studies in 18th Century Music, ed. H.C. Robbins Landon in collaboration with Robert E. Chapman, Oxford University Press, pp. 343-362.

2 Colton, S., Lopez de Mantaras, R., & Stock, O. (2009). Computational Creativity: Coming of Age. AI Magazine, 30(3), 11. https://doi.org/10.1609/aimag.v30i3.2257

3 Boden, M. A. (2009). Computer Models of Creativity. AI Magazine, 30(3), 23. https://doi.org/10.1609/aimag.v30i3.2254

4 Gervás, P. (2009). Computational Approaches to Storytelling and Creativity. AI Magazine, 30(3), 49. https://doi.org/10.1609/aimag.v30i3.2250

5 Ritchie, G. (2009). Can Computers Create Humor?. AI Magazine, 30(3), 71. https://doi.org/10.1609/aimag.v30i3.2251

6 Bushinsky, S. (2009). Deus Ex Machina — A Higher Creative Species in the Game of Chess. AI Magazine, 30(3), 63. https://doi.org/10.1609/aimag.v30i3.2255

7 Cardoso, A., Veale, T., & Wiggins, G. A. (2009). Converging on the Divergent: The History (and Future) of the International Joint Workshops in Computational Creativity. AI Magazine, 30(3), 15. https://doi.org/10.1609/aimag.v30i3.2252