# Unit 4: Intelligent Behavior

The goal of Unit 4 is for students to understand the emergence of intelligent behavior by exploring two distinct approaches: from human crowd sourcing of data and from separate algorithmic agents working in parallel. The goal is to galvanize the connections among computing concepts and between computing and society. The first lesson explores the hardware layer of computing, working from discrete components to integrated circuits. The exponential advancement of electronics, low on the ladder of abstraction, is connected to advancements at the highest levels on the ladder of abstraction, where artificial intelligence and simulation and modeling are impacting all fields. In the concluding lesson, students identify problems and questions that can be addressed with computer simulation, incorporating agent-based modeling. Students are challenged to explore the assumptions and parameters built into several simulations and to attach meaning to the results. Having explored a few applications of intelligent behavior emerging from algorithmic components, students reflect on the current and future state of artificial intelligence.

**4.1 Moore's Law and Modeling**

- In this lesson, students construct an understanding of how the explosion of technology over the last two decades has impacted every realm of study and employment. Students begin by researching the impact of computer modeling and simulation, which has been made possible by the rapid increase in computational power due to the continued applicability of Moore's Law. They then manipulate discrete electronic components to create logic gates. Students create comparable results using integrated circuits to get a feel for what it means to double the number of transistors that can fit in a given area. Students explore simulation in NetLogo directly by manipulating a model of predation and a model of the spread of viruses in humans. The lesson concludes with an examination of the code of ethics for simulationists and reflection on the necessity of adhering to such a code.

**4.2 Intelligent Agents**

- In this lesson, students experiment with materials designed to illuminate the rise of intelligent and complex behavior from simple rules and seemingly unintelligent agents. Students begin by studying a model of Langton's ant, a simple Turing machine with some surprising emergent behavior. The students manipulate artificial neurons, called perceptrons, and neural networks. Students design and conduct their own experiments on a model of their own choosing using Monte Carlo methods. Students explore the generation and observation of fractals and study a diffusion-limited aggregation model for producing fractal behavior.