Eric Tatara

Complex adaptive systems modeling with Repast Simphony

PurposeThis paper is to describe development of the features and functions of Repast Simphony, the widely used, free, and open source agent-based modeling environment that builds on the Repast 3 library. Repast Simphony was designed from the ground up with a focus on well-factored abstractions. The resulting code has a modular architecture that allows individual components such as networks, logging, and time scheduling to be replaced as needed. The Repast family of agent-based modeling software has collectively been under continuous development for more than 10 years.MethodIncludes reviewing other free and open-source modeling libraries and environments as well as describing the architecture of Repast Simphony. The architectural description includes a discussion of the Simphony application framework, the core module, ReLogo, data collection, the geographical information system, visualization, freeze drying, and third party application integration.ResultsInclude a review of several Repast Simphony applications and brief tutorial on how to use Repast Simphony to model a simple complex adaptive system.ConclusionsWe discuss opportunities for future work, including plans to provide support for increasingly large-scale modeling efforts.

An intelligent system for multivariate statistical process monitoring and diagnosis

A knowledge-based system (KBS) was designed for automated system identification, process monitoring, and diagnosis of sensor faults. The real-time KBS consists of a supervisory system using G2 KBS development software linked with external statistical modules for system identification and sensor fault diagnosis. The various statistical techniques were prototyped in MATLAB, converted to ANSI C code, and linked with the G2 Standard Interface. The KBS automatically performs all operations of data collection, identification, monitoring, and sensor fault diagnosis with little or no input from the user. Navigation throughout the KBS is via menu buttons on each user-accessible screen. Selected process variables are displayed on charts showing the history of the variables over a period of time. Multivariate statistical tests and contribution plots are also shown graphically. The KBS was evaluated using simulation studies with a polymerization reactor through a nonlinear dynamic model. Both normal operation conditions as well as conditions of process disturbances were observed to evaluate the KBS performance. Specific user-defined disturbances were added to the simulation, and the KBS correctly diagnosed both process and sensor faults when present.

Interpreting ECG data by integrating statistical and artificial intelligence tools

The use of an automated system integrating data conditioning, statistical methods, and artificial intelligence tools to summarize and interpret high-frequency physiological data such as the electrocardiogram is investigated. The development of a methodology and its associated tools for real-time patient monitoring and diagnosis is accomplished by using the commercial programming environments MATLAB and G2, a real-time knowledge-based system (KBS) development shell. Data interpretation and classification is performed by integrating statistical classification methods and knowledge-based techniques with a graphical user interface that provides quick access to the analysis results as well as the original data. A KBS was developed that incorporates various statistical methods with a rule-based decision system to detect abnormal situations, provide preliminary interpretation and diagnosis, and to report these findings to the healthcare provider.

Agent-based analysis and simulation of the consumer airline market share for Frontier Airlines

The complex and interconnected world in which organizations operate presents many challenges to the traditional neo-classical view of research and management and associated research techniques. Fundamental to the operation of financial capital markets, investor confidence relies on accurate investment analyst earnings forecasts. We propose agent-based modeling (ABM) as a viable tool to account for the interaction of local and environmental factors to determine organizational success. In an illustrative case study of Frontier Airlines, we develop and execute an ABM of Frontiers consumer airline market to derive market share for the upcoming year. In the model, Frontier is impacted by internal policies, competitors, and environmental factors of fuel costs, federal regulation, and credit availability. We conclude with a discussion on how ABM can be effectively incorporated into future research activities and decision-making situations.

A hybrid supervisory knowledge-based system for monitoring penicillin fermentation

Batch and fed-batch bioprocesses generally exhibit batch-to-batch variation. Multivariate statistical monitoring of these processes based on the use of empirical models developed from the multiway principal component analysis was performed by using contribution, T/sup 2/ and square prediction error plots. To cope with uncertainties in the fermentation process and to provide more effective supervision, a knowledge-based system was developed allowing the coupling of quantitative statistical information with the qualitative domain expertise (heuristic knowledge). This hybrid system (PenExpert) aims to perform old-of-batch process monitoring as well as online process monitoring and fault diagnosis.

Simulating agricultural land rental markets by combining agent-based models with traditional economics concepts

Land exchange through rental transactions is a central process in agricultural systems. The land tenure regimes emerge from land transactions and structural and land use changes are tied to the dynamics of the land market. We introduce LARMA, a LAnd Rental MArket model embedded within the Pampas Model (PM), an agent-based model of Argentinean agricultural systems. LARMA produces endogenous formation of land rental prices. LARMA relies on traditional economic concepts for LRP formation but addresses some drawbacks of this approach by being integrated into an agent-based model that considers heterogeneous agents interacting with one another. PM-LARMA successfully reproduced the agricultural land tenure regimes and land rental prices observed in the Pampas. Including adaptive, heterogeneous and interacting agents was critical to this success. We conclude that agent-based and traditional economic models can be successfully combined to capture complex emergent land tenure and market price patterns while simplifying the overall model design. LARMA is a land rental market model with endogenous rental price formation.LARMA is embedded into the Pampas agent-based model (PM).LARMA combines traditional economics concepts with agent-based modeling.PM-LARMA successfully reproduces land tenure patterns and rental price dynamics.We discuss the advantages of combining approaches for modeling land rental markets.

Agent-based system for reconfiguration of distributed chemical reactor network operation

Control of spatially distributed systems is a challenging problem because of their complex nature, nonlinearity, and generally high order. Agent-based control structures provide a powerful tool for managing distributed systems by utilizing local and global information obtained from the system. A hierarchical, agent-based system with local and global control agents is developed to control networks of interconnected chemical reactors hosting multiple autocatalytic species. The global controller agent dynamically updates the objective of local control agents as the reactor network conditions change. The case illustrated in this paper is to change the dominant species in one CSTR by modifying feed and interconnection flow rates with the constraint of shortest path possible, which causes the least amount of changes in the whole network. The agent-based system and the reactor networks are implemented using the agent-based system development framework RePast

Agent-Based control of spatially distributed chemical reactor networks

Large-scale spatially distributed systems provide a unique and difficult control challenge because of their nonlinearity, spatialdistribution and generally high order. The control structure for these systems tend to be both discrete and distributed as well and contain discrete and continuous elements. A layered control structure interfaced with complex arrays of sensors and actuators provides a flexible supervision and control system that can deal with local and global challenges. An adaptive agent-based control structure is presented whereby local control objectives may be changed in order to achieve the global control objective. Information is shared through a global knowledge environment that promotes the distribution of ideas through reinforcement. The performance of the agent-based control approach is illustrated in a case study where the interaction front between two competing autocatalytic species is moved from one spatial configuration to another. The multi-agent control system is able to effectively explore the parameter space of the network and intelligently manipulate the network flow rates such that the desired spatial distribution of species is achieved.

Monitoring and fault diagnosis of a polymerization reactor by interfacing knowledge-based and multivariate SPM tools

An intelligent process monitoring and fault diagnosis environment is developed by interfacing multivariate statistical process monitoring (MSPM) techniques and knowledge-based systems (KBS) for monitoring continuous multivariable process operation. The software is tested by monitoring the performance of a continuous stirred tank reactor for polymerization of vinyl acetate. The real-time KBS G2 and its diagnostic assistant (GDA) tool are integrated with MSPM methods based on canonical variate state space (CVSS) process models. Fault detection is based on T/sup 2/ of state variables and squared prediction errors (SPE) charts. Contribution plots in G2 are used for determining the process variables that have contributed to the out-of-control signal indicated by large T/sup 2/ and/or SPE values, and GDA is used to diagnose the source cause of the abnormal process behavior. The MSPM modules developed in Matlab are linked with G2 and GDA, permitting the use of MSPM tools for multivariable processes with autocorrelated data. The presentation will focus on the structure and performance of the integrated system. On-line SPM of the multivariable polymerization process is illustrated by simulation studies.

SELF-ORGANIZING AGENT-BASED GRADE TRANSITION IN DISTRIBUTED CHEMICAL REACTOR NETWORKS

Abstract Agent-based control structures provide flexible and emergent solutions to complex nonlinear problems benefiting from properties such as modularity, adaptability, scalability and robustness. One such problem is product grade transitions in distributed process. The framework proposed earlier (Tetiker, 2006a) is extended by adding several layers of agents to control species percentage distribution in autocatalytic reactor networks. A deadlock detection layer is implemented to detect and solve conflicting cases between local controller agents. An auctioning mechanism is employed to promote competition between local controller agents leading to emergent solutions satisfying global constraints. The proposed architecture performed successfully to change the species percentage distributions without specifying the final configuration.