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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