Emergence, Self-organisation, Adaptivity and Evolution - Artificial Intelligence

This section discusses several features of autonomous agents that are important from a behavioural perspective – emergent, self-organising, adaptive and evolving behaviour.

A complex system is a system comprising many components which when they interact with each other produce activity that is greater than what is possible by the components acting individually. A multiagent system is a complex system if the agents exhibit behaviours that are emergent. Emergence in a complex system is the appearance of a new higher-level property that is not a simple linear aggregate of existing properties. For example,the mass of an aeroplane is not an emergent property as it is simply the sum of the mass of the plane’s individual components. On the other hand,the ability to fly is an emergent property as this property disappears when the plane’s parts are disassembled. Emergent properties are also common in real life – for example, cultural behaviour in humans, food foraging behaviour in ants and mound building behaviour in termites. Emergent behaviour is the appearance of behaviour of a multi-agent system that was not previously observed and that is not the result of a simple linear combination of the agents’ existing behaviour.

Some people believe that intelligence is an emergent property, the result of agent – agent and agent-environment interactions of reactive, embodied, situated agents. If this is so, then this provides an alternative path for producing intelligent behaviour – rather than building cognitive agents by explicitly programming higher cognitive abilities such as reasoning and decision-making, the alternative is to build agents with reactive abilities such as pattern recognition and learning, and this will lead to intelligent behaviour as a result. This approach, however, has yet to bear fruit as the mechanisms behind humans’ pattern recognition and learning abilities is yet to be fully understood and we do not have sophisticated enough algorithms in this area for agent’s to learn the way humans do, for example, such as a young child’s ability to acquire language. However, the more traditional route to artificial intelligence – that of designing agents with explicit higher-level cognitive abilities – also has yet to bear fruit.

A system is said to self-organise when a pattern or structure in the system emerges spontaneously that was not the result of any external pressures. A multi-agent system displays self-organising behaviour as a result of applying local rules when a pattern or structure forms as a result of its interaction that was not caused by an external agent.

Self-organising systems typically display emergent properties. Many natural systems exhibit self-organising behaviour. Some examples are: swarms of birds and fish, and herds of animals such as cattle, sheep, buffalo and zebras (biology); the formation and structure of planets, stars, and galaxies (from the field of astrophysics); cloud formations and cyclones (meteorology); surface structure of the earth (geophysics); chemical reactions (chemistry); autonomous movements of robots (robotics); social networks (Internet); computer and traffic networks (technology); naturally occurring fractal patterns such as ferns, snowflakes, crystalline structures, landscapes, fiords (natural world); patterns occurring on fur, butterfly wings, insect skin and blood vessels inside the body (biology); population growth (biology); the collective behaviour of insect colonies such as termites and ants (biology); mutation and selection (evolution); and competition, stock markets and financial markets (economics).

The NetLogo Models Library contains a number of models that simulate self-organisation.For example, the Flocking model mimics flocking behaviour in birds – after running the model for some time, the turtle agents will self-organise into a few flocks where the birds all head in a similar direction. This is despite the individual agents’ behaviour only consisting of a few local rules.

In the Fireflies model, the turtle agents are able to synchronise their flashing using only interactions between adjacent agents; again only local rules define the individual agents’ behaviour.The Termites model and the State Machine Example Model simulate the behaviour of termites. After running these models for some time, the ‘wood chip’ patches will end up being placed in a few piles.Three screenshots of the State Machine Example Model are shown in Figure below. The leftmost image shows the environment at the start of the simulation (number of ticks = 0). It shows agents placed randomly throughout the environment, with the yellow patches representing the wood chips, and the white shapes representing the termites. The middle and right most images shows the environment after 5,000 and 50,000 ticks, respectively. The orange shapes represent termites that are carrying wood chips, the white shapes those that are not. The two images show the system of termite agents, wood chips and the environment progressively self-organising so that the wood chips end up in a few piles.

The State Machine Example Model simulates self-organising behaviour for termites.

State Machine Example Model simulates self-organising behaviour for termites

The code for the State Machine Example Model is shown in NetLogo Code .

NetLogo Code : Code defining the state machine example model

The setup procedure randomly distributes the yellow patch agents and the termite agents throughout the environment. The ask command in the go procedure defines the behaviour of the termite agents. The approach used is to represent the behaviour as a finite state machine consisting of four states with a different action or task that the agent performs providing the transition to the next state. These tasks are: searching for a wood chip; finding a new pile; putting down a wood chip; and getting out of the pile.

A system in general sense is said to evolve if it adapts or changes over time usually from a simple to a more complex form. The term ‘evolve’ has different meanings in different contexts and this can cause some confusion. A more specific meaning relates the term ‘evolve’ to Darwin’s theory of evolution – a species is said to evolve when a change occurs in the DNA of its population from one generation to the next. The change is passed down to offspring through reproduction. These changes may be small, but over many generations, the combined effects can lead to substantial changes in the organisms.

In order to differentiate the different meanings of the term ‘evolve’, we can define adaptive and evolving behaviour separately in the following way. An agent exhibits adaptive behaviour when it has the ability to change its behaviour in some way in response to changes in the environment. If the environment changes, behaviour that is well-adapted to the previous environment may no longer be so well-adapted.

Evolving behaviour, on the other hand, occurs in a population when its genetic makeup has changed from one generation to the next. Evolution in a population is driven by two major mechanisms – natural selection and genetic drift. Natural selection is a process whereby individuals with inheritable traits that are helpful for reproduction and survival in the environment will become more common in the population, whereas harmful traits will become more rare. Genetic drift is the change in the relative frequency of inheritable traits due to the role of chance in determining which individuals survive and reproduce. Evolution of humans and animal species occurs over hundreds of thousands of years, and sometimes millions. To put these time scales into perspective, and to illustrate how small changes can have epoch-changing effects, we can use the example of the Himalaya Mountain Range.

A fault line stretches from one end of the Himalayas to the other as it sits on the boundary between the Eurasian and Indo-Australian tectonic plates, and as a consequence it is one of the most seismically active regions in the world. Studies have shown that the Himalayas are still rising at the rate of about 1cm per year. Although a 1cm rise per year may seem negligible, if we project this far into the future, then the accumulative effect can be remarkable. After 100 years,it will have risen by only a metre; after 1000 years, 10m; after 10000 years, just 100m,still not especially significant when compared to the overall average height of the mountain range. However, after 100,000 years, it will have risen by 1 km – that is over 10% of the current height of Mt. Everest which is 8,848 metres. After a million years, the rise in height will be 10 km, which more than doubles the current height of Mt. Everest.

A process that produces very little change from year to year, if continual,will produce dramatic changes over the course of a million years.Mt. Everest rising constantly for a million years is clearly a hypothetical situation because there are other forces at work such as erosion and tectonic plate movement.In contrast,the rise of the seas, even by as small amount as 1cm per year, can result in dramatic change in a much shorter period of time. Continental drift has also caused significant change in the world’s landscape. The flight distance between Sydney, Australia and Wellington, New Zealand, for example, is 2220 km. If New Zealand has been moving apart from Australia at the rate of 1 cm per year, then this has occurred over a period of 222 million years.

No matter how well suited a particular species may be at surviving in its current environment, it will need to adapt to epoch-level changes if it is to survive for a very long time.

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