Two aspects of background knowledge including the models of creativity and compu- tational creativity, and the relationship between language and creativity as well as the related computational models are reviewed. Firstly, the models of creativity related with cognitive behaviours and social interactions are described and the systems models of so- cial creativity, in particular, Domain-Individual-Field-Interaction Framework (DIFI) are reviewed. Then, the computational models of individual creativity and social creativity, and the evaluation of computational creativity were discussed. Secondly, the relation- ships between language and creativity are described. The evolution of language and the features of language related with creativity including ambiguity, diversity and composi- tion etc., are discussed and the computational models of language and social creativity consisting of imitation game, guessing game and generation game are presented.
Linguistic researchers mainly focus on evolving language and exploring the originality of language but not consider the creative features related with generating new com- positional concepts and transmitting creative design information between individuals. Design researchers normally take language as a medium of processing design or study design language such as shape grammar. In other words, the connection between lan- guage and design is mainly related with design expression and description that lacks deep understanding of the creative role of language played in design communication. This thesis tries to fill the research gap between communication and creative design via multi-agent simulations of the evolution of language in design contexts, and take lan- guage as a creative social tool for design more than a medium, design syntax or artifact semantics.
The next chapter presents a computational model building on the background research covered in this chapter to provide a foundation for the experiments presented in Chapters 4,5 &6.
Computational Model
This chapter describes a computational model of evolving language for creative design
based on the DIFI framework (see Sections 2.1.3.2 and 2.3.2) and the implementation
of this model used to simulate language games for social creativity in the experiments described in the following chapters. Firstly, a computational model of the DIFI frame-
work (Saunders, 2002) is presented. Secondly, the computational model expands on
earlier attempts completed bySaunders and Grace(2008) to model the DIFI framework
by incorporating different types of interactions such as guessing/learning, encoding/de- coding, and generating/producing in the evolution of language. At last, the implemen- tations of the computational model makes use of artificial neural networks including Self-Organising Maps (SOM), Adaptive Resonance Theory (ART) networks and Grow- ing Neural Gases (GNG), to generate symbols and transmit information through the communication of multi-agents with both short-term memory (STM) and long-term memory (LTM).
To evolve artificial languages for knowledge representation and creative designing, a com- putational framework is developed to implement three processes consisting of represen- tation, communication and evaluation. The first is to generate “utterances” and match them with some existing design knowledge, e.g. the generation of consonant-vowel pairs matching different combinations of topics such as colours and shapes using associative neural networks. It requires the establishment of appropriate representations of design knowledge and “utterances”. The second is to produce new “utterances” by recombin- ing existing artificial utterances and transforming them to new design concepts related to novel design spaces via the communication of client-agents and designer-agents, e.g. “house” → “movable house” (recreational vehicle). Effective communication requires the selection of suitable computational algorithms to satisfy the requirements of compli- cated transformations, i.e., the mapping from utterances to concepts (design knowledge) through inductive means in language games. Finally, the criteria for evaluating design creativity are relative to the design requirements. Appropriate criteria for the evaluation of design creativity include criteria for the evaluation of designs, e.g. novelty and value
considerations, or the process of designing, e.g. novel techniques. In this model, one of important criteria for the evaluation of creativity is the unexpectedness of a design given its description in the form of an utterance. Unexpectedness is determined by an
agent’s ability to predict an as-yet-unseen event (Saunders,2002). For example, the pre-
dictions made by client-agents about future designs do not match the designs provided by designer-agents in generation games. Such unexpectedness could indicate novelty.
3.1
A Computational Model of the DIFI Framework
The computational model presented here is based on the Domain-Individual-Field-
Interaction (DIFI) framework (see Fig. 3.1). In this computational model, agents
develop an artificial language by negotiating and obtaining a mainly group-accepted,
e.g. more than 70%1, scheme that maps utterances to design features. Divergent gener-
ation of works is completed by individuals; convergent collection is made by field; and individuals are trained by exposure to a domain. Some parts of the scheme will be different in each agent’s associative memories although for communicative success they are likely to share a significant part of the language. The individual differences can be the sources of social creativity in multi-agent simulations.
Figure 3.1: The Domain-Individual-Field-Interaction (DIFI) framework (Saunders after Csikszentmihalyi)
3.1.1 Domain
The computational model of a domain is a repository for shared knowledge, including the accepted design works and shared symbolic representations, e.g. the domain-specific
languages developed to improve communication between agents (Saunders,2011). The
languages include simple design grammars and relevant semantics that reflect design
structures and features particularly in the experiments,Incongruity(see Section5.3) and
Extensibility and Other Features (see Section 5.4). Different domains or sub-domains
1
can be subsequently used as initial settings for the production of new generations of
designs (Saunders,2011). The interaction of domains is implemented in the experiment,
Clique Formation (see Section6.3).
Design knowledge is composed of a number of rules such as repetition, variation, con- trast, rhythm, self similarity etc. For example, the specific constructing rules of rec- tilinear volumes mainly include piercing, wedging and cradling. These structures can be represented by utterance combinations such as “co-we-ya” and “fe-ge-ge”, which are
generated and shared by interactions between individuals, field and domain (Saunders,
2011). And these combinations can be mapped to different designs.
Domain knowledge can be generated in a connectionist model to impose constraints on
solutions such as Boolean operations on geometric shapes in the experiment, Compo-
sitional Language for Shape Combination (see Section 4.3). The domain knowledge is
distributed among individuals rather than being stored in the central repository that in such cases the domain is considered to be that part of the learned meaning of utterances
that is shared by a significant proportion of a field (Saunders,2011).
Designs and languages are shared in a public or local domain. Different clients can have different evaluation criteria. Some clients can even adopt the failed works rejected by other clients that results into local domain and clique formation.
3.1.2 Individual
The types of individuals include adaptive agents and curious agents. The former such as listener-agents evolve basic domain languages in guessing games while the latter includ- ing client-agents and designer-agents evolve creative design briefs and works respectively in generation games. Curious agents are capable of evaluating and selecting topics, utter- ances and designs, and extracting interesting features based on the evaluation of novelty using hedonic functions.
In guessing games, agents can also be curious when selecting interesting topics or gener- ating novel utterances representing these topics. A curious speaker-agent is capable of selecting a novel but not too novel topic by comparing current topics with the topics it has experienced in using hedonic functions. It can also choose a unique topic from cur- rent topics by comparing their features such as the differences of shapes, colours or sizes. Thus an interesting topic can be selected according to its novelty and distinctiveness. A novel utterance can also be generated by selecting a different combination of characters compared with the utterances which are stored in its memory, or by combining some of these stored utterances to a new compositional utterance.
In generation games, designer-agents learn basic rules and methods from the domain, then generate new works and modify them to satisfy clients’ requirements in the field.
Agents adjust the preference of communication and categorise topics using short-term
memory and long-term memory respectively (Saunders,2002).
The process of communication among multi-agents is generating, exchanging and eval- uating messages between addressers and receivers in various contextual environments
(Lidov,1999). Statements are mainly generated by initiator/speaker-agents and client-
agents. These statements constitute several features such as ambiguity, scalability and extensibility, which can be used to generate novel works by designer-agents. These works are sent to client-agents for evaluation. The repeated communications among agents lead to semantic assimilation and grow a full-blown artificial language by generating utter-
ances and expanding relevant meanings (Saunders,2011).
3.1.3 Field
Field is responsible for assessing generated designs, extracting interesting works, and sending them to the Domain. The assessment criteria identify the difference between
objects and the relationships among them (Hannah,2002). The evaluations of generated
works obey some common criteria such as similar-yet-different although different indi- viduals make various judgments according to their own experiences and roles. Not only the designs generated by designer-agents but also the utterances generated by speaker- agents and client-agents can be evaluated. For example, “babadula” is more interesting than “babababa” as a design brief. Such accumulated successful works and relevant representations evaluated and extracted from the Field enrich the Domain.
In the computational model of the DIFI framework, individuals learn about the common knowledge of the Domain while retaining their personalities due to the differences in the
sequence of experiences that they have, i.e., the situatedness of the agents (Gero and
Fujii,2000). This leads to sustainable creative behaviours by exchanging knowledge and
re-generating new knowledge. Emergence occurs via large scale communications from
local niches (Saunders,2002) via the evolution of language in the Field.