Topics in Artificial Intelligence

By Danny Kovach

Existing Methods ofArtificial 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 ofBiological 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. 1 Movie 1

Swarm Intelligencewith 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 = αf(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 3 Movie 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 withConstraints

• Particle kinematics is particularly useful in terrain coverage problems with constraints.

• Examples of an attractive force with a constraint:

Movie 6 Movie 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 MemoryStructure (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

References

References