If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Creativity requires the generation of novel ideas, a process often called insight. A model of this process is proposed. At the macro-level, the model is a standard symbolic architecture for problem solving. Its problem perception component consists of a layered network of processing units. At the micro-level, each such unit propagates its computational result along a subset of its forward links. The subset is selected by matching the activation levels of the links to a threshold for forward propagation. The activation levels are subject to feedback adjustment and redistribution. A simple simulation demonstrates that these characteristics suffice to produce sudden alterations in a unit's output. The question whether a network consisting of such units will exhibit this behavior is raised.
Dance choreography is a system of techniques used to create new dances. The choreographer devises body movements using internal and external cues to express feelings and concepts, from the most abstract ideas to very concrete human situations in a highly creative manner. In 3D Tele-immersive Environments (3DTI) the choreographer has exponentially more options to create new body movements in the new dance since the 3DTI technology offers an array of visual stimulations, called Digital Options, which influence this movement making process. In this paper, first, we explore the creative process of dance choreography through Laban/Bartenieff Movement Analysis (LMA) representation via computational models in the 3D technology. Second, we elaborate on the creativity framework and the design of dynamic compositions that are placed in the geographically distributed multi-stream, multiparty 3DTI environment. Third, we discuss some very preliminary results of our creativity framework and first findings that validate parts of our computational modeling and 3DTI design.
In this article we have classified computational creativity research activities into three generations. Although the respective system developers were not necessarily targeting their research for computational creativity, we consider their works as contribution to this emerging field. Possibly, the first recognition of the implication of intelligent systems toward the creativity came with an AAAI Spring Symposium on AI and Creativity (Dartnall and Kim, 1993). We have here tried to chart the progress of the field by describing some sample projects. Our hope is that this article will provide some direction to the interested researchers and help creating a vision for the community.
Creativity is often associated with surprise, novelty, usefulness and value. These characteristics do not assist in the development of a model for intelligent systems to achieve creative behaviour, since they are characteristics that help identify when something or someone has been creative, as post-facto evaluation. Rather, models of creative behaviour for intelligent systems draw on process models such as analogical reasoning and induction, or on principles such as "make the familiar strange" or "make the strange familiar". This paper describes how a computational model of curiosity, based on cognitive models of novelty and interest, can be used to focus attention in learning agents. We show how this combination of curiosity and learning can be the core reasoning process in agent-based systems that achieve creative behaviour.
It is proposed that creativity can be enhanced through the use of interactive genetic algorithms (IGAs). Divergent and convergent thinking are important processes in creativity that we simulate through two separate IGA populations developed by different means. The convergent process hones in on specific designs, while the divergent process explore design possibilities in a fashion beyond pure mutation techniques typically used to introduce population diversity. This study uses Monte Carlo simulation to explore the effect of merging two populations developed by the divergent and convergent methods. The results suggest that population diversity benefits from these population combinations while not adversely affecting the ability of the user to find a goal design. This IGA has also been developed in Adobe Flash so that it can be deployed on the internet to conduct validation and studies of creativity.
Though creativity is usually defined as "novel and appropriate," this is most often understood to mean "as novel as possible, so long as appropriate." While this definition might be suitably applied to finished products, it is less obviously useful as a guiding value during the act of creation. This research tests this and other definitions by using computer simulations based on Campbell's "blind variation and selective retention" theory. Introducing a "temperature" parameter to reduce novelty's importance over time produces results superior to both an even combination of novelty and appropriateness and the prevailing "novel, so long as appropriate" definition. However, choosing the correct temperature adjustment schedule is essential.
This paper describes an artificial creative system that simulates basic creative design behavior through the use of pseudo-genetic design supplemented by human recognition and evaluation. While it remains unclear if the system is truly creative itself, it provides the necessary support structure for a design platform that reshapes creative decision making as a question of design growth rather than manufacture.
We add to the discussion of how to assess the creativity of programs which generate artefacts such as poems, theorems, paintings, melodies, etc. To do so, we first review some existing frameworks for assessing artefact generation programs. Then, drawing on our experience of building both a mathematical discovery system and an automated painter, we argue that it is not appropriate to base the assessment of a system on its output alone, and that the way it produces artefacts also needs to be taken into account. We suggest a simple framework within which the behaviour of a program can be categorised and described which may add to the perception of creativity in the system.
The concept of ambiguity is often discussed within the field of Artificial Intelligence; however, its role and effect on early-stage complex problem solving is not well understood. This paper describes a theoretical framework that recognizes the relationship between ambiguity and uncertainty, as these variables change throughout the different stages of problem solving. We particularly focus on the start of the process, when ambiguity and creativity are typically high. Through a case study approach, we hope to provide a foundation for the design and development of creative intelligent systems that more effectively support ambiguity during complex problem solving.
We address some issues concerning the relationship between metaphor and creativity, arising from an AI project (ATT-Meta) that has developed a partiallyimplemented theory of metaphor understanding. The central claim is that, while metaphor is plausibly based partly on structured mappings (analogies) between a target subject matter (the topic actually being addressed) and some source subject matter, creative metaphorical phraseology very often does not require construction of new mapping links to handle source aspects used by the phraseology but not handled by existing mappings. Thus, such phraseology often rests on substantial nonparallelism (non-analogy) between source and target. Rather, the unmapped source aspects serve only to indirectly control what information is transferred between source and target by already-known mapping links; and the phraseology, while itself creative, is not causing a grandly creative new look at the target. However, the approach explains relatively easily the nature of much creative metaphor and how it can be understood, and has some implications for creativity more generally.