Power and limits of reactive intelligence Chapter 5 of Evolutionary Robotics Jan. 16, 2007 BumJoo Lee Robot Intelligence Technology Lab.

Contents Introduction Perceptual aliasing Simplification hard problems Exploiting behavioral attractors Exploiting constraints Conclusions Robot Intelligence Technology Lab.

1. Introduction Reactive system Sensory-motor coordination Directly linked sensors and motors Reacting to the same sensory state with the same motor action Limited role in internal states Rejecting the importance of internal representation Sensory-motor coordination Mapping from the sensory patterns to actions that modify the position of the agent with respect to the external environment and/or modify the external environment itself. Robot Intelligence Technology Lab.

2. Perceptual aliasing Perceptual aliasing Situation wherein two or more objects generate the same sensory pattern, but require different responses [Whitehead and Ballard 1991]. Solution to this situation Performing actions in order to search for other sensory information that is not affected by the aliasing problem Performing actions that maximize the chance to select an unambiguous sensory pattern Active perception Process of selecting sensory patterns which are easy to discriminate through motor actions [Bajcsy 1988] Robot Intelligence Technology Lab.

3. Simplification hard problems Type-2 problems [Clark and Thornton 1997] Hard tasks in which the problem to map input patterns into appropriate output patterns is complicated by the fact that the regularities, which can allow such a mapping, are marginal or hidden in the sensory patterns Type-1 (tractable) problems Many regularities which are directly avalable in the sensory patterns Reduction of type-2 problems to type-1 problems Recoding sensory inputs [Elman 1993] Training a simpler subtask and then entire task Sensory-motor coordination [Scheier et al. 1998] Actively structuring inputs Experimented with Khepera robot Robot Intelligence Technology Lab.

3. Simplification hard problems Experiments by passive network Passively discrimination between the sensory patterns produced by the two objects: wall vs. small cylinder or small vs. large cylinder Training a neural network with 3 architectures 6 neurons corresponding to the 6 frontal infrared sensor for input layer and 1 neuron for output layer Additional internal layer of 4 units Additional internal layer of 8 units Trained by back-propagation algorithm < The robot and the envronment > Robot Intelligence Technology Lab.

3. Simplification hard problems Experimental results Mostly unable to discriminate type 2 problem < Relative position where networks are able to correctly discriminate Left: wall vs. small cylinder, right: small vs. large cylinder x-axis: angle, y-axis: distance [mm] > < Percentage of successfully classification > Robot Intelligence Technology Lab.

3. Simplification hard problems Experiments by sensory-motor coordination Approaching large and avoiding small cylinders Artificial evolution is used to select the weights of the neural controllers Performances are increased and stabilized after 40 generation GSI (Geometric Separability Index) [Thornton 1997] f: 0 = small 1 = large cylinder xi: sensory pattern xi'’: the nearest neighbor sensory pattern 0.5: completely overlap 1:well seperated < Performance and GSI > Robot Intelligence Technology Lab.

4. Exploiting behavioral attractors Experiments: another hard perceptual discrimination Discrimination between walls and a cylinder by finding and remaining close to the cylinder 6 sensory neurons and 2 output neurons for the speed of the two wheels Attractor area: never stop in front of the target, but moving back and forth, and left and right Robot Intelligence Technology Lab.

4. Exploiting behavioral attractors Simulated agent To better clarify the role of sensory-motor coordination in this emergent strategy (attractor area) 40 cells (20 on the left and 20 on the right) labeled numbers (0~19) with random orders Weights: 0 (cw), 1 (ccw) Aim: staying left side Robot Intelligence Technology Lab.

4. Exploiting behavioral attractors Attractor area All sensory state: 50 % to be in the left or in the right Impossible to select sensory states which are not affedted by the aliasing problem Solving by produce attractors Robot Intelligence Technology Lab.

5. Exploiting constraints Rats and food experiments Locating buried food in the same position with visible food patch Rats and food experiments with robot Rectangluar 60x30 cm environment 8 sensory neurons and 2 motor neurons Robot Intelligence Technology Lab.

5. Exploiting constraints Results Reduction the number of constraints available in the envirionment  harder task and worse poerformance < Top: random starting position, orientations and size of the environment Bottom: random proportion between long and short walls Left vs. Right: average results and best results > < Behavior of a typical evolved robot > Robot Intelligence Technology Lab.

Conclusions Selection sensory patterns which are bit affected by the aliasing problem avoidance those Selection sensory patterns for which groups of patterns requiring different answers do not overlap too much Exploitation behavioral attractors resulting from the interaction between the robot and the environment Exploitation the constraints present in the environment by using sensory-motor coordination Robot Intelligence Technology Lab.