Laboratory for Computational Intelligence, University of British Columbia Belief & Decision Networks Stochastic Local Search Neural NetworksGraph Searching Robot Controller About CIspace Planning Student Researchers: Saleema Amershi, Nicole Arksey, Mike Cline, Wesley Coelho, Kevin O'Neill, Michael Pavlin, Joseph Roy Santos, Shinjiro Sueda, Leslie Tung, Audrey Yap Project Supervisors: Cristina Conati, Peter Gorniak, Holger Hoos, Alan Mackworth, David Poole CIspace is a collection of interactive software tools for learning fundamental principles in the field of Computational Intelligence. supports various levels of abstraction for first-time and experienced users supports a wide variety of presentation modes and learning styles can be used for in-class demonstration, as an exploratory environment, or within interactive tutorials Decision Tree NetworkDefinite Clause Deduction Consistency-Based CSP Solver Tools for learning computational intelligence This applet is designed to introduce different search strategies for searching directed graphs. Colored nodes represent different node types. Users can learn five different algorithms. Frontier information changes as users step through the graph. Quiz tests user’s knowledge. In Solve mode, a graph represents the planning problem. Users can watch an animated graphical representation of the graph as the planner solves the problem. This applet demonstrates how to generate a plan to rearrange blocks stacked on a table. In Create mode, users create a block world graphically. This applet illustrates the reasoning and decision-making power of Belief Networks, also called Bayesian or Causal Networks, and Decision Networks. Users can examine the query method at two different levels of abstraction. Users can ask or be asked conditional independence questions. Tables show probability or utility details. Nodes can be monitored for changes. The purpose of this applet is to illustrate how the process of answer extraction within a knowledge base can be cast as a search problem. Creating a new query is easy with interactive dialog boxes. Users can refer to the knowledge base while stepping through tree. This applet demonstrates how to build a decision tree using a training data set and then use the tree to classify unseen examples in a test data set. Users can select node information to be display. Plot displays error information visually. This applet demonstrates learning in a neural network and can be used to predict outcomes for any set of input values. Colors indicate the different weights. The error plot demonstrates how learning rates and target error values can be altered to affect performance. Users input a set of values and can view the calculated output from the trained network. This applet is designed to help you learn about solving CSPs with a systematic search technique called arc consistency. Domain splitting history traces user choices. This applet illustrates and provides tools for the analysis of local search algorithms while demonstrating another approach for solving CSPs. Plots display searching statistics graphically. Users can browse through algorithm descriptions and options before deciding which algorithm to use while searching. This applet simulates a robot perceiving and acting under the control of a set of customizable robot controller functions. Users can modify the robot’s controller code that drives the robot through its environment. Users can watch the robot execute its user- defined plan. Controller logs show communication between different layers. Part of the online resources for the book Computational Intelligence: A Logical Approach, by Poole, Mackworth, and Goebel Users can view the search tree as it is being built. Users can inspect and prune domains of selected variables. Colored arcs help users visualize constraints before, during and after being made arc- consistent.