Artificial Life Simulations Research Articles

Teaching OOP in C++ using an artificial life framework

This paper explores the framework method for teaching object-oriented programming. Specifically, it describes a hierarchy of C++ classes that implement the framework for an Artificial Life simulator. Students learn how to read these classes and extend them via inheritance: they design and implement subclasses that encapsulate the behavior and state of environments and the entities that inhabit them. The simulator constructs an artificial world from objects of these subclasses; then, it animates these objects as they interact during the simulation. This paper includes one sample project and both subclasses that implement its solution.

A Markov Model of Production, Trade, and Money: Theory and Artificial Life Simulation

The paper generalizes the Kiyotaki-Wright trade model by treating the trading period as a finite game, so Nash‘s theorem can be used to prove the existence of equilibrium, and by treating the economy as a Markov process, so an ergodic theorem can be used to show the existence of equilibria with desirable properties (e.g., in which money exists). A Markov model of trade also allows us to add complexity to the economy without adding corresponding complexity to the analysis of the model‘s properties. The paper also provides artificial life simulations of the Markov economy suggesting that monetary equilibria are dynamically stable and do not require high levels of learning or information processing on the part of agents.

Great organizing principles

Great organizing principles

Frpm artificial individuals to global patterns

Artificial Life is a model of biological systems that describes lives archived by computer simulation, chemical substrates or any other non-biological substrates. Artificial Life simulation adopts a bottom-up approach in which behavior of lower-level entities (e.g. molecules, cells and individuals) is all that is programed; global patterns (e.g. evolutionary patterns observed at the level of the population and the community) can emerge as a result of interaction among lower-level entities. Artificial Life simulations will be used not only to test ecological and evolutionary hypotheses explaining real organisms but also to show the validity of general theories, processes and concepts such as natural selection, theories of complexity, hierarchical relations and self-organization.

Engaging students and teaching modern concepts

This paper describes our experience in using situated programming to deliver modern computer science concepts in the introductory programming course at Carnegie Mellon University. We used an artificial life simulation and taught object-oriented programming as well as more traditional material. The course was an experience, not an experiment, since many aspects of the course simultaneously changed from prior offerings. Nevertheless, what we saw was fundamental and potentially far-reaching. The most important result was that students were intellectually engaged. They came to grips with basic object-oriented programming, they mastered the topics of procedural programming, they learned first hand about computer simulation, they learned perhaps a bit about biology. Most importantly, through it all they used programming to express and explore their own powerful and novel ideas.

Dynascope

This paper introduces program directing , a new way of program interaction. Directing enables one program, the director , to monitor and to control another program, the executor . One important application of program directing is human interaction with complex computer simulations. The Dynascope programming environment is designed specifically to support directing in traditional, compiled languages. It provides a framework and building blocks for easy construction of sophisticated directors. Directors are regular programs that perform the directing of executors through Dynascope primitives. Dynascope is built around the concept of the execution stream which provides a complete description of the executor's computational behavior. The source code of executors requires no changes in order to be subjected to directing. This paper gives an overview of the Dynascope system. Sample applications are presented: debugging register allocation, animation of procedure calls, and a complex artificial life simulation. The performance of Dynascope is illustrated by real time measurements.