LaMothe continues stating that, “A personality is nothing more than a collection of predictable behaviors.” And later, “…you should be able to model personality types using logic and probability distributions that track a few behavioral traits and place a probability on each.”
LaMothe also suggests adding a “radii of influence” that further adjusts and influences a computer personality (see below):
Modeling Memory and Learning with Software
This is a fairly obvious AI routine with the addition of agents transferring knowledge and data. See the sample source code for memory and learning. (Note: Word may not correctly open the file which is a Visual Studio C++ file.)
Planning and Decision Trees
LaMothe states that high-level AI = planning = a high-level set of actions. He also suggests building a decision tree of nodes that represent a production rule and/or an action which can be loaded at runtime from a file.
Pathfinding
LaMothe begins by describing basic, primitive pathfinding algorithms including “Trial and Error.”
Contour Tracing
This method involves constantly shooting a ‘line’ from the subject to the goal. Whenever an obstacle is encountered a new path is traced around it:
As LeMothe says, “This works, but it looks a little stupid because the algorithm traces around things instead of going through the obvious shortest path. But it works.” But there’s something to be said for an algorithm that is guaranteed to work.
Collision Avoidance Tracks
This method involves calculating (possibly pre-computing), or ‘drawing’ during level design, avoidance paths around all obstacles and storing them for later use. See diagram below:
This system is fine if you have the luxury of pre-computing or pre-designing terrain.
Waypoint Pathfinding
This method consists of constructing a series of predefined waypoints and paths (see diagram below). Source code is also included showing implementation of this method in a driving/racing game.
Robust Pathfinding
Breadth-First, Bidirectional Breadth-First, Depth-First, Dijkstra’s Search and A* Searches are described. However, LaMothe cautions that none of these are applicable to real-time pathfinding.
Advanced AI Scripting
LaMothe describes some basic scripting techniques and suggests using the compiler pre-processor commands to turn a pseudo-English scripting language into standard C/C++ code. The script file (xxx.scr) would be added as an include to the code and then compiled and linked into the executable.
Artificial Neural Networks
Neural Nets have been around since they were first described by McCulloch and Pitts in 1943. A neural net basically emulates the human brain by creating logic circuits that fire when two inputs are greater than a predetermined value (theta). A bias can also be added to simulate long term memory. See LaMothe’s in depth article here (neural.doc). Also in the same subdirectory is source code and executable examples of neural network implementation.
Genetic Algorithms
LaMothe only briefly describes Genetic Algorithms that emulate the process of “natural selection” to solve AI tasks. Obviously, the key to creating robust genetic algorithms rests with the algorithm evaluation criteria.
Fuzzy Set Theory, Fuzzy Linguistic Variables and Rules, Fuzzy Manifolds and
Membership, Fuzzy Associative Matrices
Key Fuzzy Logic terms and concepts:
DOM = Degree of Membership
FLV = Fuzzy Linguistic Variable
FAM = Fuzzy Associative Matrix (Matrices)
Logical AND means minimum of the set
Logical OR means maximum of the set
The key principles of fuzzy logic are:
