Chapter 14 Emergent behaviours in autonomous robots B. Hutt, K. Warwick & I. Goodhew Department of Cybernetics, University of Reading, UK. Abstract It is well known from the field of artificial life that complex and life-like behaviours can be generated by apparently simple systems. Such behaviours are considered as being emergent, as the simple systems that generate the behaviours do not themselves explicitly mention the behaviour generated. Using such an approach it is possible to generate complex life-like behaviours in robots by merely following a simple set of rules. Flocking in birds, herding in cattle and shoaling in fish are all common examples of emergent coordinated group behaviours found in nature. Despite the apparent complexity of these behaviours, it is possible to convincingly synthesise such behaviours in robots using only simple sensors and programming. The evolution of life itself can be considered as an emergent process with intelligence and many other interesting properties emerging through the mechanisms of evolution. Algorithms based on the mechanisms of evolution have been used with great success to evolve robot behaviours that are doubly emergent – where the evolved behaviour is itself an emergent property of the evolutionary process and the implementation of that behaviour is also emergent, as it is the product of simple rule following that does not mention the behaviour. 1 Introduction The natural world is teeming with a vast range of different biological organisms many of them exhibiting extremely complex behaviours. It is well known that apparently complex and life-like behaviours can be generated in artificial systems by surprisingly simple systems – a good example of this is Conway’s game of life [1]. In Conway’s game high-level patterns and structures form from extremely simple low-level rules. The game of life is played out on an infinitely large grid of square cells rather like a chessboard. In this system every cell in the grid is surrounded by eight other cells; with each cell on the grid in one of two states – dead or alive. It is the number of live cells surrounding each cell that determines the next state of the cell. The rules that govern the game of life are as follows – a dead cell with exactly three live neighbours becomes a live cell, a live cell with two or three live neighbours remains alive, in all other cases the cell becomes (or remains) dead. The elaborate patterns that can WIT Transactions on State of the Art in Science and Engineering, Vol 27, © 2006 WIT Press www.witpress.com, ISSN 1755-8336 (on-line) doi:10.2495/978-1-85312-853-0/14 448 Design and Information in Biology be produced are really quite surprising when we consider they are purely the result of following such simple rules – the game of life is one of the simplest examples of emergent complexity. 2 Complexity from simplicity – emergent behaviour from simple rules Amongst the earliest work to explore the complexity of behaviour that could be produced with simple artificial systems is the work of William Grey Walter. 2.1 Early reactive robots In 1948 William Grey Walter built his first ‘tortoise’robot. Walter was interested in the complexity of behaviour that minimally simple robots were capable of producing. Consequently, the ‘brain’ of his tortoise robot was deliberately restricted to just two functional elements (miniature radio valves). In order to discover what degree of complexity of behaviour and independence could be achieved with the smallest number of elements in a system providing the greatest number of intercon- nections [2]. The robot was based on a three wheeled (tricycle shaped) chassis enclosed by a hard shell – it had two motors one driving both rear wheels, in order to propel the robot, and the other controlling the front steering wheel. It had only two sensors; a photocell mounted on top of the steering column, at the front of the robot, pointing in the same direction as the steering wheel and a contact switch attached via the shell so that when an object pushed on the shell it closed a contact enabling collisions to be detected. Each tortoise had two basic reflexes (or ‘instincts’) that enabled it to head towards sources of light and negotiate obstacles. The primary, light seeking, behaviour used the photocell to search for and drive the robot towards sources of light, however, if the light source was too strong it caused the robot to steer away from the light instead of towards it. The second behaviour, obstacle avoidance, was triggered by the contact sensor, which caused the tortoise to retreat and then sidestep, thereby allowing the robot to avoid or push aside obstacles. The robots behaved in a phototropic manner somewhat analogous to flight paths of moths – executing graceful curves as they searched the environment for sources of light and retreating when they encountered an obstacle or got too close to a light source. Walter’s next robot was even more complex, having the ability to learn a simple conditioned response. This robot possessed an additional module ‘bolted on’ to the back of his previous robot which created a connection between the robot’s light reflex, its contact reflex and a third sensor that detected when a whistle was blown. The robot could be trained by blowing the whistle and then kicking the robot initiating the obstacle avoidance reflex. After five or six beatings, whenever the whistle was blown [the robot] turned and backed from an ‘imagined’ obstacle [3]. The level of autonomy and behavioural complexity achieved by Walter’s tortoise robots is really quite surprising, especially considering the technological limitations of the day. Walter had produced a proof of concept that complex behaviours can stem from the interaction of a few ingeniously connected but simple parts [4]. These machines are perhaps the simplest that can be said to resemble animals. Crude though they are, they give an eerie impression of purposefulness, independence and spontaneity [2]. WIT Transactions on State of the Art in Science and Engineering, Vol 27, © 2006 WIT Press www.witpress.com, ISSN 1755-8336 (on-line) Emergent Behaviours in Autonomous Robots 449 2.2 Thought experiments with simple robots Unfortunately it appears that the significance of Walter’s work was not properly understood at the time and it was not until the mid 1980’s that researchers once again started to consider the poten- tial capabilities of simple mobile autonomous robots [5]. Through a set of thought experiments Braitenberg investigated the properties of a number of simple robot controllers capable of generat- ing unexpectedly complex behaviours. He argued that if we did not know the simple principles by which the robots were functioning we might call such behaviours ‘aggression’, ‘love’, ‘foresight’ and even ‘optimism’. Interestingly, Braitenberg also considered the possibility of using evolution to design robot controllers as well as the effects of Hebbian style learning. 3 Modern reactive robots In the mid 1980’s Rodney Brook’s group at MIT began to examine the performance of simple layered controllers with real robots using his subsumption architecture ([6, 7]). Robots designed and programmed using such methods are capable of exploring a typical office environment in real time, reacting to and avoiding obstacles with few problems. The performance of such robots was significantly better than other mobile robots reportedly in existence at the time so that in [8] the following claim was (rather modestly) made: We claim as of mid-1987 our robots, using the subsumption architecture to implement complete Creatures, are the most reactive real-time mobile robots in existence. Most other mobile robots are still at the stage of ‘experimental run’in static environments, or at best in completely mapped static environments. Ours, on the other hand, operate completely autonomously in complex dynamic environments at the flick of their on switches, and continue until their batteries are drained [8]. 3.1 Reactive robots Essential to these systems is the concept of emergent behaviour, i.e. the idea that a behaviour is often the by-product of simple rule following that does not specifically mention the behaviour exhibited. Basic Braitenberg vehicles of the type shown in Fig. 1 provide an extremely simple example of emergent behaviour, such vehicles are incredibly minimal but can exhibit surprisingly complex behaviour [5]. Some schematics of basic Braitenberg vehicles are shown in Fig. 1. Two different vehicles are shown. Each vehicle has two light-sensitive sensors and two motors, with which to move. The sensors are used to directly control the motors, such that if a sensor is activated by a light source its output enables power to be supplied to a connected motor. There are two obvious possibili- ties, either the sensors can control the motors on the same side of the vehicle as the sensor, or they can control the motors on the opposite side. When exposed to a light source the vehicle pictured on the left in Fig. 1 will try to evade it, whilst the vehicle shown on the right will tend to approach it. There is no explicit mention of any light evading or following goal in such systems, rather the behaviour is implicit in the structure of the robot itself and the way in which its body allows it to interact with the environment. More importantly there is no representation or internal model of the world and therefore there is no separate perception, planning or action. Effectively the control system is entirely decentralised. WIT Transactions on State of the Art in Science and Engineering, Vol 27, © 2006 WIT Press www.witpress.com, ISSN 1755-8336 (on-line) 450 Design and Information in Biology Figure 1: Braitenberg vehicle schematic.