Topics in Artificial Intelligence By Danny Kovach

Existing Methods of Artificial Intelligence (AI) Intelligence refers to a set of properties of the mind. –From a psychological perspective, it is defined as the "overall capacity to think rationally, act purposefully, and deal effectively with the environment." [Coon, 2000]. Biologically Inspired AI – Attempts to develop a form of AI by mimicking biological processes. –Called scruffy because results are less provable in a formal sense, as opposed to neat techniques that are provable formally. Evolutionary Algorithms – Use evolutionary concepts to achieve some goal. –Population – Initial set of test solutions. –Reproduction – Means by which to create subsequent populations, or generations. –Heredity – Means by which information can be passed to subsequent generations. –Stopping criterion.

Popular Forms of Biological AI Genetic Algorithms Swarm Intelligence Neural Networks

Swarm Intelligence Also called particle swarm optimization (PSO). A population or swarm of particles moves about the solution space. –Each particle or agent contains the following. Position Velocity Best Position (Local) Best Position (Global) Every agent is updated as the algorithm iterates. Runs until stopping criteria are met.

Swarm Intelligence Can be used to find the minima of functions such as that of figure 1. An example is shown in movie 1. Fig. 1Movie 1

Swarm Intelligence with Force Functions Employs slightly more dynamic particle motion based on particle kinematics (equations of motion ) from classical physics Each agent is updated as follows: Acceleration parameter comes from a force function Variables are initialized as follows –a0 comes from force function –v0 chosen randomly –x0 specified

Force Functions Can be functions of particle position and velocity Can have forces between particles (pheromones). Focus on functions of the form F = α f(x) By manipulating the function f and the parameter α, we can tailor the force to be attractive, repulsive, or zero. Example of a particle swarm with zero force: Movie 2

Attractive Force Functions Attractive functions are used in optimization problems. Weaker force functions cover more terrain, but convergence is slow Examples of attractive forces: Movie 3Movie 4

Repulsive Force Functions Repulsive force functions can be used in terrain coverage problems, when a particular area has been well covered. Examples of repulsive forces: Movie 5

Force Functions with Constraints Particle kinematics is particularly useful in terrain coverage problems with constraints. Examples of an attractive force with a constraint: Movie 6Movie 7

The Dynamic Memory Structure (DMS) Began as a NASA funded project for the purpose of vibration control and analysis Algorithm scans for mechanical vibrations which are harmful to equipment so that we can dampen them

An Overview of Memory Assume we have a collection of elements Theory - the Mathematics of Memory –Distance Function Relates elements within the structure –Topology Structure generated by the distance function Elements classified into neighborhoods –Fitness Function Evaluates the “goodness” of the elements with respect to the problem at hand Application –Structure of the DMS –Sorting elements With respect to the distance function By the fitness function –Will provide an example of the DMS in AI

Inducing a Topology Using the distance function, we can organize the elements in memory into a structure. Can adjust coarseness and fineness, the “resolution” of the structure. Figure 2 shows graphical representations of the memory structure Fig. 2

Organizing the MS A linear search can be very time consuming. We will organize the MS to aid signal recognition as follows –Choose an element in the MS, called the pivot. –Calculate the distance between all elements and the pivot using h. –Arrange all signals into a vector according to their distance via h. Call this structure the derived memory structure. Organizing the structure can help with convergence (finding things) Fig. 3

The Dynamic Memory Structure (DMS) We can employ the above theory to create the DMS. The DMS can –Dynamically allocate elements in memory –Resort itself with respect to changes –Keep track of the recollections of elements –Adjust internal tolerance parameters Applications in AI –Problem – Ant is seeking food and at the same time learning about its terrain. –Why? Can adapt to changes in the environment Deal with obstacles –Initial position of ant and food are given –The ant searches the terrain, opting to explore parts it hasn’t encountered Movie 8

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