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Group learning offers novel yet intriguing opportunities to improve a community’s effectiveness by obtaining insight into the emergence of new design problems and concepts. This article proposes a new computational model and the related algorithmic methods to characterize circuit design communities over time. The bottom–up model explains knowledge evolution using two operators, combination/improvement for knowledge expansion, and blocking for concept elimination. New metrics were proposed to describe the effect of the two operators. Experiments considered three large data sets of circuit designs (switched-capacitor filters, CMOS OpAmp/OTAs, and $\Delta \Sigma $ ADCs). Opportunities to improve the communities were discussed.

Text embedding has recently emerged as a very useful and successful method for semantic representation. Following initial word-level embedding methods such as Latent Semantic Analysis (LSA) and topic-based bag-of-words approaches like Latent Dirichlet Allocation (LDA), the focus has turned to language models and text encoders implemented as neural networks - ranging from word-level models to those embedding whole documents. The distinctive feature of these models is their ability to infer semantic spaces at all levels based purely on data, with no need for complexities such as syntactic analysis or ontology building. Many of these models are available pre-trained on enormous amounts of data, providing downstream applications with general-purpose semantic spaces. In particular, embedding models at the sentence level or higher are most useful in applications because the meaning of text only becomes clear at that level. Most text embedding methods produce text embeddings in high-dimensional spaces, with a dimensionality ranging from a few hundred to thousands. However, it is often useful to visualize semantic spaces in very low dimension, which requires the use of dimensionality reduction methods. It is not clear what language models and what method of dimensionality reduction would work well in these cases. In this paper, we compare four text embedding methods in combination with three methods of dimensionality reduction to map three related real-world datasets comprising textual descriptions of items in a particular domain (sports) to a 2-dimensional semantic visualization space. The results provide several insights into the utility of these methods for data of this type.

Understanding the process of producing creative responses to open-ended problems solved in small groups is important for many modern domains, like health care, manufacturing, education, banking, and investment. Some of the main theoretical challenges include characterizing and measuring the dynamics of responses, relating social and individual aspects in group problem solving, incorporating soft skills (e.g., experience, social aspects, and emotions) to the theory of decision making in groups, and understanding the evolution of processes guided by soft utilities (hard-to-quantify utilities), e.g., social interactions and emotional rewards. This paper presents a novel theoretical model (TM) that describes the process of solving open-ended problems in small groups. It mathematically presents the connection between group member characteristics, interactions in a group, group knowledge evolution, and overall novelty of the responses created by a group as a whole. Each member is modeled as an agent with local knowledge, a way of interpreting the knowledge, resources, social skills, and emotional levels associated to problem goals and concepts. Five solving strategies can be employed by an agent to generate new knowledge. Group responses form a solution space, in which responses are grouped into categories based on their similarity and organized in abstraction levels. The solution space includes concrete features and samples, as well as the causal sequences that logically connect concepts with each other. The model was used to explain how member characteristics, e.g., the degree to which their knowledge is similar, relate to the solution novelty of the group. Model validation compared model simulations against results obtained through behavioral experiments with teams of human subjects, and suggests that TMs are a useful tool in improving the effectiveness of small teams.

In creativity research a typical problem is that of assessing the novelty of ideas or solutions generated by many people to open ended problems. For datasets larger than a few hundreds, human assessment of novelty becomes time consuming and error prone. Existing novelty detection methods such as: distance based text similarity or language model approaches do not work well for small datasets. Moreover, when compared to human novelty ratings, these approaches fail to capture the same cognitive processes or biases. We are proposing a novel cognitive model inspired by a leaky accumulator decision making models for detecting novel ideas from short text. The model is applied on a collection of ideas generated in a group brainstorming experiment. It evaluates an idea term by term and it accumulates surprise and relevance. The final novelty decision is taken at the end of each idea by means of a threshold. An important component of the model is a small domain dataset which is used to evaluate the surprise of a term’s context compared to common domain knowledge. The model is compared with other methods: feature based classifiers, tf-idf similarity distance, and pretrained language models (ULMFIT).

This paper presents InnovA, a cognitive architecture for creative problem solving in analog circuit design, e.g., topology creation (synthesis), incremental topology modification, and design knowledge identification and reuse. The architectural modules attempt to replicate cognitive human activities, like concept formation, comparison, and concept combination. The architecture uses multiple knowledge representations organized using topological similarity and causality information. Solutions are clustered, so that each cluster represents a specific set of performance tradeoffs, thus a fragment of the Pareto front in the solution space. New structural features are created through variation of existing features. New solutions are created by combining features from the same cluster, distinct clusters, and features that originate a new cluster. The related algorithms are discussed in this paper too. The architecture also incorporates modules mimicking the using of human emotions in memory formation and decision making, but these modules are still under development and are a main direction for our future work. This paper presents a number of examples to illustrate its using in various analog circuit design activities that are hard to be realized with traditional computational methods.

Understanding creativity and innovation in large communities (collectivities) is emerging as a key problem in computational and social sciences as the amount and importance of innovation achieved by groups significantly exceeds innovation produced by individuals. Community-level innovation raises new, intriguing yet challenging problems (e.g., knowledge and belief formation, learning, and decision-making at the community level). This chapter presents and contrasts computational modeling approaches for studying creativity in large communities, from cognitive models to multiagent models and machine learning models. It also details a new model for gaining insight on the evolution process that leads to innovative solutions by large communities. The model has the following components: (1) explicit knowledge representation, (2) expression of the evolution process, and (3) a backward differential reasoning method for characterizing the trajectory of the evolution process. The chapter presents an application of the proposed model as a cyber-social system for reducing fixation in communities working on related problems.

Semantic analysis of text corpora is of broad utility, including for data from conversations, on-line chats, brainstorming sessions, comments on blogs, etc. – all of which are potentially interesting sources of information and ideas. In the present paper, we look at data from a large group brainstorming experiment that generated thousands of mostly brief statements. The ultimate goal is to detect which statements are semantically atypical within the overall corpus. In contexts such as spam detection or detection of on-line intrusions, autoencoders have been used successfully to separate typical from atypical data, and we consider this approach in the present paper. Texts are embedded in a semantic space obtained through topic analysis, and an autoencoder network is used to reconstruct each embedded text. The results show that, while difficulty of reconstruction is related to quantitative measures of atypicality in the embedding vector space, it is not well correlated with novelty assignments made by a human rater. However, this is not the case when the data is first clustered in the embedding space: The reconstruction error for each data cluster indicates that some clusters represent more novel data than others, and that the inverse size of the cluster and the mean reconstruction error of the texts in the cluster capture this well. In particular, autoencoders that enforce dimensionality reduction improve discrimination. The results also show that, in the reconstruction process, the autoencoder implicitly discovers the same clusters in the data that are discovered explicitly by an optimized k-means approach.

Mining engineering metaknowledge from electronic documents is important in devising new design solutions for emerging applications as well as in identifying new business opportunities. Continuously and comprehensively reviewing and assessing the entire technical literature to find the most relevant ideas and solutions is cumbersome as engineering innovations are constantly being proposed. In particular, high performance analog circuit design has been at the forefront of technological innovations given their importance in applications, like telecommunications, mobile applications, and health care. This paper proposes a model and mining techniques for representing analog circuit design metaknowledge. The metaknowledge representation includes three components: an associative component presenting the conceptual hierarchy of the considered circuits, a component expressing the performance capabilities (e.g., tradeoffs and bottlenecks) of the circuits, and a causal modeling part describing the more likely starting ideas and design plans used to create a circuit. Knowledge structures can be used to tackle new applications, identify opportunities to improve existing circuits, and validate design correctness. A case study illustrates the proposed knowledge structure for a set of 30 modern, high-frequency analog circuits.

This paper presents a model for semantic network representation. According to the model, the main purpose of a semantic network is to help to correctly and quickly understand the features and structure of the solutions, and then make reliable predictions about their outcomes and rewards. These objectives are achieved under constrains like limited resources (e.g., memory size and access), time limitations, and variations of solution characteristics. The paper summarizes the elements of the model and presents a set of properties that describe the effectiveness of semantic networks in creative problem solving, including design. Six examples (based on three case studies from the related literature and three experimental studies conducted by the authors) discuss the model elements, characteristics, and properties.

This paper discusses the nature of concept combinations in modular design of electronic embedded systems as well as the relation between combination characteristics and novelty, quality, and usefulness of the produced solutions. Through two experimental studies, this work explored the frequency of relation-based and property-based combinations in embedded design solutions, and how the specifics of the given building blocks, i.e. salience, relatedness and number, influenced the produced combinations. The impact of popular aids, like titles and short descriptions (briefs), in improving novelty, quality, and usefulness of the designs was also analyzed. Design solutions include mostly relation-based combinations. Design novelty correlates mainly to the purpose and context of the produced combinations. Novelty is aided by titles but not by briefs.

This paper describes an axiomatic model to express concept structures in analog circuit design. The concepts of a structure represent sets of circuit designs, and are defined based on the similarities and differences in their circuit topology, behavior and performance. Similarities and differences are found through concept matching, a main operator of the concept structure. A concept structure is extended with new concepts that are created by combining the features of existing concepts and by exploring new relations between variables through instantiation. Concepts are characterized by a set of metrics, e.g., variety, utility, novelty, complexity, flexibility, constraining factor, and bottleneck. The paper presents a novel circuit design methodology as an application of the model. Two case studies illustrate the utility of the axiomatic model in expressing knowledge structures for various types of circuits.

Many creative activities take place in a group context, whether in short-term meetings, work teams, or by means of electronic interaction. The group creative process necessarily involves the exchange of ideas or information. Recent models of group creativity have focused on the cognitive underpinnings of this type of group creative process, primarily based on the group brainstorming literature. The authors describe an elaborated computational version of their cognitive model of group creativity and related computational models, and highlight some plausible neural bases for various involved processes. The major findings and theoretical perspectives in this literature are summarized and some potentially fruitful empirical and theoretical directions are highlighted. It is hoped that this comprehensive treatment can be a basis for integrating the present literature and providing useful predictions for further research on this topic.