The Role of Artificial Life, Cellular Automata and Emergence in the study of Artificial Intelligence Ognen Spiroski CITY Liberal Studies 2005

Presentation Outline Artificial Intelligence and Artificial Life Complex Systems and Artificial Life –Emergence –Self-organization and the edge of chaos The Digital Worlds of Artificial Life Cellular Automata –Conway’s Game of Life

Artificial Intelligence and Artificial Life Definition of AI –AI is the study of the mechanisms underlying intelligent behaviour through the construction and evaluation of artifacts that attempt to enact those mechanisms Classic AI –Logic based, symbol manipulating –Top-down models simulate intelligent behaviour

Artificial Intelligence and Artificial Life (cont’d) Behaviour-oriented AI –Inspired by biology “defines intelligence in terms of observed behaviour and self-preservation. It is based on the idea that the essence of biological systems is their capacity to continuously preserve and adapt themselves” –Bottom-up computational models result in emergent behaviour

Artificial Intelligence and Artificial Life (cont’d) Behaviour-oriented AI paradigms Connectionist Evolutionary Agent-based Emergent –Don’t rely on a central scheme which explicitly describes the intelligent behaviour –Through the interactions of many simple structures, complex behaviour that is not directly programmed is exhibited

Defining Artificial Life Definition –the analysis and study of life and life-like processes in man-made systems through the use of simulation and synthesis Why? –Broaden understanding of what life is by building it artificially –Explore synthetic evolution –Life-as-it-could-be vs Life-as-we-know-it

Defining Life The problem of defining what life “is” Life is built by simple, non-living components Yet it appears to be more than the mere sum of their interaction Traditional definitions –Test for certain properties: Metabolism, adaptability, self-maintenance, autonomy, growth, replicability, evolution, etc. –Incomplete

Defining Life – Complex Systems Complex Systems –Life is found in complex dynamic systems –Life requires a certain level of complexity in a dynamic system Defining the threshold of complexity which separates living from non-living systems Life - an emergent phenomenon in a complex system

Emergence Defining emergence –“The theory of emergence involves three propositions: (1) that there are levels of existence... (2) that there are marks which distinguish these levels from one another... (3) that it is impossible to deduce marks of a higher level from those of a lower level... ” –To define life as an emergent phenomenon implies the acknowledgement that different properties of systems require different, qualitatively unrelated, epistemological categories and models, which cannot be reduced to the properties of the component parts of the system.

Artificial Life and Emergence Search for the origin of life –The threshold of complexity needed for the emergence of life Understand life through computational emergence

Self-organization A hallmark of emergence Definition the spontaneous formation of well organized structures, patterns, or behaviors, from random initial conditions Found in complex dynamical systems these systems tend to reach a particular state, or a set of cycling states, or a small volume of their state space, with no external interference. All the mechanisms dictating its behavior are internal to the system: self-organization as opposed to externally imposed organization

The Edge of Chaos Ordered systems –Not enough complexity for life to emerge Chaotic systems –Too rapid changing to self-organize sufficient complexity and sustain life Complex systems –Life is found at the edge of chaos (Langton) Phases found in dynamical systems

The Digital Worlds of Artificial Life Christopher G. Langton: "The principle assumption made in Artificial Life is that the 'logical form' of an organism can be separated from its material basis of construction, and that 'aliveness' will be found to be a property of the former, not of the latter.“ Life in a logical informational universe

The Digital Worlds of Artificial Life Simulations in the digital medium –Ease of research Well known formal structure Data gathering Completely repeatable experiments –Fast Is it “real life”?

Cellular Automata Definition –“A cellular automaton is a discrete dynamical system. Space, time, and the states of the system are discrete. Each point in a regular spatial lattice, called a cell, can have any one of a finite number of states. The states of the cells in the lattice are updated according to a local rule. That is, the state of a cell at a given time depends only on its own state one time step previously, and the states of its nearby neighbors at the previous time step. All cells on the lattice are updated synchronously. Thus the state of the entire lattice advances in discrete time steps. “ An example of complex systems

Cellular Automata History History –von Neumann and self-reproducing automata –Conway’s “Game of Life” –Wolfram – the Universe as a CA

The Game of Life Invented by mathematician John Conway The best known example of a CA –Very simple, yet –An excellent example of emergence and self-organization

Game of Life - Definition A two-state two-dimensional CA with three very simple rules of action: 1. One inactive cell surrounded by three active cells becomes active ("it's born") 2. One active cell surrounded by 2 or 3 active cells remains active 3. In any other case, the cell "dies" or remains inactive.

Game of Life - Examples Still life objects (block, beehive, boat, ship, loaf) They simply remain the same in the next generation Oscillators They change throughout generations, but essentially cycle through the same pattern Gliders They move diagonally across the grid The Queen Bee Shuttle Spaceships They move left, right, up or down instead of on diagonals like gliders …

Game of Life - Research A completely known universe Computation is possible –A Turing machine has been implemented –Formal proof that self-reproducing mechanisms are possible …

Summary Artificial Life: –Biology inspired –Multidisciplinary approach –Computational evolution Complex Systems –Emergence and self-organization –Life at the edge of chaos Cellular Automata –Simple, yet effective models for studies in Artificial Life