CS 536 - Situated Computing

Bulletin Description

This course covers the fundamental concepts involved in understanding and engineering a closed-loop, sensing, reasoning, and actuating agent. Biological models of sensing and actuation will be discussed and related to modern artificial counterparts. The course consists of three major topic areas: Vision, Brain, and Robotics. It will introduce students to the issues in computer and biological vision, to models of belief representation and modification, architectures for percept processing and reasoning, machine learning for vision, neural networks, path planning, intelligent localization based on visual cues, and to forward and inverse kinematics, intelligent grasping, and the integration of perception and action.


CS-460G or consent of instructor.

Expected Preparation

Students should be familiar with topics in artificial intelligence problem solving methods, including logic, search, min-max game theory, and basic knowledge representation schemes. Students should show the mathematical maturity expected of first-year graduate students.

Student Learning Outcomes

Students will learn a broad base of concepts dealing with the design and implementation of a situated agent capable of sensing and acting intelligently on its world. The core concepts learned will be computer vision, models of intelligent computation in both machines and biological systems, and robotics. Students will be asked to implement a specific agent in the "Bugs World" simulator, based on Braitenburg vehicles. Student designed agents will use vision, reasoning, and control to compete for resources within the simulator, while avoiding predators. A variety of readings in conjunction with lectures will introduce students to the theoretical foundations of artificial, intelligent, agents. Students will understand the central issues in engineering an intelligent system as well as the different philosophical frameworks that have historically been used to address the problem of constructing a rational, situated agent.


Direct Measures:

Students are evaluated on their work (homeworks, projects and exams).

Students receive back their homework and exams. These papers are marked to indicate problems and they point out correct or better solutions. Problems that turn out to be especially difficult are discussed in class during lectures.

Different numerical scales are used for graduate students and undergraduate students. For graduate students, we use the following scale:

90 - 100


80 - 89


70 - 79


60 - 69


0 - 59


For undergraduate students we use the following scale:

86 - 100


76 - 85


66 - 75


56 - 65


0 - 55


Syllabus Information

Possible Textbooks:

There is no single textbook that covers the topic sufficiently. A collection of readings and course notes is the primary source of material for the course. Possible supplemental reading:

Active Robot Vision: Camera Heads, Model Based Navigation and Reactive Control,
H. I. Christensen (ed.), World Scientific Pub Co; 1993.

Vision, Brain, and Cooperative Computation.
Michael Arbib and Allen Hanson (eds). MIT Press, 1987.

The Artificial Life Route to Artificial Intelligence: Building Embodied, Situated Agents.,
Luc Steels and Rodney Brooks, Lawrence Erlbaum Assoc., 1995.