Gary R. Mayer

Composable Cellular Automata

Cellular automata (CA) provide a convenient approach to modeling a system comprised of homogeneous entities that, generally, have a spatial relationship with one another. CA are used to model systems that can be appropriately represented as a collection of interconnected automata. These networked automata may act as either a model representation of the entire system, or used to model a sub-system within a hybrid system. As the sub-systems within a hybrid system are disparate, so too can the models representing them be disparate using a multi-model approach. However, to take advantage of multi-modeling, CA and other models used to represent the sub-systems must be founded on system-theoretical principles. Furthermore, each model’s formalism must account for input and output data exchange with other modeling formalisms. Therefore, to support modular synthesis of distinct CA models with non-CA models, a composable cellular automata (CCA) formalism is proposed. This formalism is provided as a domain-neutral, mathematical specification. The CCA is then exemplified as part of a multi-model, and the GRASS development environment is used to describe one possible implementation approach.

Managing Hybrid Model Composition Complexity: Human–Environment Simulation Models

Multimodeling approaches are increasingly required for simulating multifaceted systems across many scientific disciplines. Such approaches represent the system as a set of subsystem models, each with its own structure and behavior. Some multimodeling approaches use modeling methods to define how the subsystem structures and behaviors interact. However, modeling a system this way brings about subsystem and composition complexity that must be managed. The complexities of hybrid models resulting from the interactions of the composed models can be reduced using interaction models. Independently developing and utilizing such interaction models provides additional flexibility in system model design, modification, and execution for both the subsystem models and the resultant hybrid system model. This chapter discusses the use of the polyformalism model composition approach for researching human–environment dynamics with direct support for managing the complexity, which results from subsystem model interactions within this domain.

A Composable Discrete-Time Cellular Automaton Formalism

Existing Cellular Automata formalisms do not consider heterogenous composition of models. Simulations that are grounded in a suitable modeling formalism offer unique benefits as compared with those that are developed using an adhoc combination of modeling concepts and implementation techniques. The emerging and extensive use of CA in simulating complex heterogeneous network systems heightens the importance of formal model specification. An extended discrete-time CA modeling formalism is developed in the context of hybrid modeling with support for external interactions.

A novel visual CA modeling approach and its realization in CoSMoS

Many important scientific and engineering problems are studied using Cellular Automata (CA) models. Mathematical formulae with component-based modeling concepts are used to specify CA models. Computer simulation tools support viewing the dynamics of CA simulation models. Existing approaches with their tools are restrictive from visual model development perspective since models must be specified in programming languages or use pre-built models. In this paper, the novel concept of spatial-specialization CA modeling is developed and introduced into CoSMoS (Component-based System Modeler and Simulator) -- a logical, visual, and persistence modeling and simulation framework. Modelers can visually create and manipulate structures of a family of hierarchical component-based CA models. The tool supports persistent models and semi-automatic parallel DEVS-based simulation code.

Building a hybrid DEVS and GRASS model using a composable cellular automaton

Modeling and simulation is pervasive throughout many different disciplines. As computing technology has provided more capability, the systems being modeled and simulated have grown larger and more complex. Often times, these large systems are managed as interacting subsystems. When it is necessary for the simulation to allow disparate subsystems to maintain their independence, then a hybrid model of the subsystems should be used. Furthermore, to ease the burden of verification and validation of simulation results, a proven system theoretical modeling specification should be used. However, many communities have already adopted nonsystem theoretical software solutions and established a group of domain experts familiar with these tools. This paper provides two things: a formal approach to building a hybrid model, and a discussion of how to incorporate a nonsystem theoretical software implementation into a proven framework. The first is done through the implementation of a Knowledge Interchange Broker (KIB) as an Interaction Model (IM). The second is accomplished by exemplifying the use of the IM in an agent-environment hybrid model. In the hybrid model, the agent is implemented in the Discrete-event System (DEVS) specification and the environment is implemented in the Geographical Resources Analysis Support System (GRASS) using a Composable Cellular Automaton (CCA) specification. This concept has been successfully applied to both example models and an interdisciplinary research project where the interactions between human activities and landscape processes are studied.

Blended Learning Training for Mentors of STEM Team Competitions

This paper describes the findings of a research study of a blended-learning approach to train mentors of teams in the Botball® Educational Robotics Program. Botball is an international team-based robotics competition for secondary students designed to build skills in computer programming, robotics, teamwork, and problem solving. For this study, we recruited new teams comprising 8-10 middle school students per team and a mentor. Teams were randomly assigned to one of three treatment groups or a control group. Mentors of teams in the experimental groups received training in one of three types of mentor practices: best practices, mentoring for self-efficacy, or a combination of best practices and self-efficacy. The training format consisted of web-based self-paced tutorials, a face-to-face workshop, and webinars. Dependent variables were student post-test scores on three assessments: Efficacy for Science-Related Jobs, STEM Achievement-Related Choices, and STEM Self-Efficacy. A priori statistical analyses showed no difference between the groups; however, post hoc analyses showed that the use of self-efficacy techniques was positively related to the three dependent measures. Post-competition surveys of mentor practices indicated that students in the treatment groups did not appear to receive distinctly different treatments, revealing some of the potential challenges of the blended learning approach for professional development of teachermentors.