The role of self-organization for the synthesis and understanding of behavioral systems CHAPTER I. of EVOLUTIONARY ROBOTICS Stefano Nolfi and Dario Floreano Naveen Suresh Kuppuswamy January 10, 2007 Robot Intelligence Technology Lab.

CONTENTS Introduction 2. Engineering Perspective Behavior based robotics Robot Learning Artificial Life 2. Engineering Perspective 3. Ethological Perspective 4. Biological Perspective Incremental Evolution Extracting supervision from environment through lifetime learning Development and evolution of evolvability 5. Conclusions Robot Intelligence Technology Lab.

1. INTRODUCTION The basic idea Robot Intelligence Technology Lab.

1.a. Behavior Based Robotics Robot is provided with a collection of simple basic behaviors, global behavior emerges through the interaction between these and environment. Coordination of behaviors maybe competitive or cooperative Design is by Trial and Error process Breakdown into simpler behaviors is done by designer intuitively unlike evolutionary robotics which is self-organised. Robot Intelligence Technology Lab.

1.b.Robot Learning Control systems (neural networks) trained with incomplete data and then generalize acquired knowledge to new circumstances. Neural systems either perform mapping between sensory input and motor state, or develop subsystems of controllers. Different learning algorithms provide different constraints on architecture and supervision required by designer. Evolutionary robotics is similar since it’s a form of learning but differs in following respects Amount of supervision is much lower No constraints on what can be part of the self-organisation process Robot Intelligence Technology Lab.

1.c. Artificial Life Artificial life tries to understand all phenomena though their reproduction in artificial systems (usually as a simulation on a computer) Relies on theory of complex dynamical systems (global properties at one level emerge from the interaction of a number of similar elements at lower elements). Evolutionary robotics is similar but uses physical devices instead of simulations Robot Intelligence Technology Lab.

2. Engineering Perspective Behavioral systems such as robots are difficult to design Behavior is the emergent property of motor interaction with the environment While simple robots can produce complex behavior, its difficult to predict which rules produce a given behavior. Conventional strategy: divide and conquer (breakdown in to perception, planning and action) Brooks approach : desired behavior broken in simpler basic behaviors modulated through a coordination mechanism The latter is more successful but still designer must decide on breaking down. Evolutionary Robotics treat system as a whole and its global behavior thus releasing burden on designer. Robot Intelligence Technology Lab.

2. Engineering Perspective (contd.) Robot Intelligence Technology Lab.

3. Ethological Perspective Robot Intelligence Technology Lab.

4. Biological Perspective Evolutionary robotics and biology have the common interest in trying to understand the success of natural evolution. Evolutionary Robotics concerns itself with identifying the conditions under which evolutionary process may select right individuals This is done through Incremental evolution through competition between species. Extracting supervision from the environment through lifetime learning Including genotype to phenotype mapping. Robot Intelligence Technology Lab.

4.a.Incremental Evolution An issue is how artificial evolution can select individuals which have competencies to solve complex tasks. For simpler tasks this can be solved by designing a suitable fitness criterion. For complex problems this is not possible because of bootstrap problem. One solution is to increase the amount of supervision Another is to start the evolutionary process with a simplified version of the task and progressively increase complexity by modifying selection criteria – incremental evolution. Robot Intelligence Technology Lab.

4.d Extracting Supervision from the environment External environment does not provide direct cue on how agent should act to achieve a goal Individuals maybe supervised richly but less explicitly (ontogenetic) or evaluated only once on how adapted they were throughout their lifetime (phylogenetic). In ontogenetic evolution information is received from environment throughout lifetime, but this huge amount of information can only indirectly transformed into a measure of how agent is doing. Individuals may not start with a general strategy but adapt throughout lifetime (plastic general) or they might have a general strategy suitable for all environments (full general). Full general is preferable but not always possible. Robot Intelligence Technology Lab.

4.e. Development and evolution of evolvability For adaptation systems must posses evolvability (ability of random variation to sometimes produce improvements). This depends on representation problem or genotype-to-phenotype mapping problem. It may be a simple one-to-one (one gene encoding a single character) or complex with several levels of organization Growing recursive instructions Plasticity Genotypes that vary in length. This mapping problem is still not clear and its not yet possible to convert this problem itself to evolutionary process. Robot Intelligence Technology Lab.

6. CONCLUSIONS The main characteristic making evolutionary robotics suitable for study of adaptable behavior is the reliance on self-organization. From an Engineering point of view simplifies a designers job. From a point of view of study of natural systems it may help us understand how natural organisms produce adaptive behavior. By scaling up to more complex tasks we may be able to explain the emergence of extraordinary variety of life-forms present on our planet. Robot Intelligence Technology Lab.