A novel agent-based, evolutionary model for expressing the dynamics of creative open-problem solving in small groups

Abstract

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.

Dynamic Diagnosis of the Progress and Shortcomings of Student Learning using Machine Learning based on Cognitive, Social, and Emotional Features

“…The sequence of interactions between a student solving a problem and the automated cue generation system is depicted in Figure 5(e) and modeled by equation (23). Its behavior is similar to a dialog system [66], in which one agent is the discriminator (generates cues) and another agent the generator (generates responses to the cues). The goal of the interaction is to minimize the semantic distance between the reference code and the student's code.…”

“…Figures 5(b)-(c) illustrates the generator and discriminator networks of the implementation. The prototype of the two agent systems was recently discussed in [66,67].…”

Studying Consensus and Disagreement during Problem Solving in Teams through Learning and Response Generation Agents Model

“…In this approach, joint action represents the interaction among team members that aim to achieve common goals, intentions, or ground [1,8,22]. Interactions coordinate members through synchronization, entrainment, alignment, and convergence [6,10] and depend on the conversational context and the team members' intentions and features [23]. For example, affiliative conversations have different interaction characteristics than argumentative interactions.…”

“…Figure 1a summarizes the process through which LRGAs create new responses during their interactions. Responses can be created through two flows [6,[45][46][47]: a fast, top-down flow and a slow, bottom-up flow. The fast flow reacts to the received inputs, present emotions, social interactions, and drawn attention to create responses that incrementally change the input or experience.…”

“…Problem framing might require finding creative solutions to improve the healthcare system in the USA [4] or making learning in a university setting more fun [3]. It must find the requirements that are collectively perceived by a group as important and useful [5][6][7]. Similarly, goal identification searches for the common goals that inspire and drive a group and thus have value (utility) for the participants [8].…”