Quelea uses the word ‘system’ in place of what would be called a swarm in previous swarm intelligence writings. Swarm seems to bring with it a notion of a large number of undesirable small insects whereas in Quelea, systems can have as few as one quelea which could represent any number of different quelea types from particles to people. To name systems swarms in Quelea, it would impose a predetemined notion of what to do or what the quelea in the systems should represent. The word ‘system’ on the other hand evokes a notion of intelligently designed interconnected nodes which could be almost anything from a particle system, immune system to a planetary system. Even a system of people seems to make more sense than swarms of people because system brings with it a notion of connectivity.This much looser definition fits the description of the idea of systems in Quelea much more aptly than that of ‘swarm.’

 

Intelligence
-“arises from interactions among individuals.” (xiii, Kennedy et al., 2001)

Heuristics
-“shortcuts to speed up the process of sorting through possibilities.” (xiv, Kennedy et al., 2001)

Swarm
-“refers to a disorganized cluster of moving things, usually insects, moving irregularly, chaotically, somehow staying together even while all of them move in apparent random directions.” (xvi, Kennedy et al., 2001)

Particle
-“massless and volumeless mathematical abstractions and would be called ‘points’ if they stayed still;”
-“velocities and accelerations are more appropriately applied to particles, even if each is defined to have arbitrarily small mass and volume.” (xx, Kennedy et al., 2001)

Particle Systems (Reeves, 1983)
-“clouds of primitive particles as models of diffuse objects such as clouds, fire, and smoke within a computer graphics framework.” (xx, Kennedy et al., 2001)

Cellular Automata (CA)
-“used for self-generated computer graphics movies, simulating biological systems and physical phenomena, designing massively parallel computers, and most importantly for basic research into the characteristics of complex dynamic systems.
-3 main attributes
-(1) individual cell updates are done in parallel, (2) each new cell value depends only on the old values of the cell and its neighbors, and (3) all cells are updated using the same rules. (Rucker, 1999).
(xvii, Kennedy et al., 2001)

Computational Intelligence
-“include hybrids of evolutionary computation, fuzzy logic, neural networks and artificial life. Central to the concept of computational intelligence is system adaptation that enables or facilitates intelligent behavior in complex and changing environments.” (xxi, Kennedy et al., 2001)

Soft Computing
-“include hybrids of evolutionary computation, fuzzy logic, neural networks and artificial life. Included in soft computing is the softening ‘parameterization’ of operations such as AND, OR, and NOT.” (xxi, Kennedy et al., 2001)

Evolutionary Computation
-“comprises machine learning optimization and classification paradigms roughly based on mechanisms of evolution such as biological genetics and natural selection (Eberhart, Simpson, and Dobbins, 1996). The evolutionary computation field includes genetic algorithms, evolutionary programming, genetic programming, and evolutionary strategies, in addition to the new kid on the block: particle swarm optimization.” (xxvi, Kennedy et al., 2001)

Mind
-“is a term we use in the ordinary sense, which is of course not very well defined. Generally, mind is “that which thinks.” David Chalmers helps us out by noting that the colloquial use of the concept f mind really contains two aspects, which he calls ‘phenomenological” and “psychological.” The phenomenological aspect of mind has to do with the conscious experience of thinking, what it is like to think, while the psychological aspect has to do with the function of thinking, the information processing that results in observable behavior.” (xxvi, Kennedy et al. 2001)

Swarm
-“a population of interacting elements that is able to optimize some global objective through collaborative search of a space. Interactions that are relatively local (topologically) are often emphasized. There is a general stochastic (or chaotic) tendency in a swarm for individuals to move toward a center of mass in the population on critical dimensions, resulting in convergence on an optimum.” (xxvii., Kennedy et al., 2001)

Artificial Neural Network (ANN)
-“an analysis paradigm that is roughly modeled after the massively parallel structure of the brain. It simulates a highly interconnected, parallel computational structure with many relatively simple individual processing elements (PEs) (Eberhart, Simpson, and Dobbins, 1996).” (xxvii, Kennedy et al., 2001)

Autonomous Agents
– “I define an autonomous agent as a system that is able toreproduce and also able to carry out at least one thermodynamic work cycle.” (Kauffman, 1996)