Artificial Intelligence. Outline

Transcription

1 Artificial Intelligence Embodied Intelligence (R. Brooks, MIT) Outline Key perspectives for thinking about how an intelligent system interacts with world Compare mainstream AI to early artificial creature approaches Derive number of morals from comparison Look at some simple animals to see how they operate in their worlds Making comparisons to artificial creatures 2 1

2 GOFAI (Good Old Fashioned AI) Traditional AI approach was/is Identify essence of something Study that, expecting to generalize back to full concept later Playing with blocks Microworld: the blocks world Ignores untidiness of real world Only the essence of building simple block towers is considered Everything represented in a neat, logical calculus to describe a 2-D scene Blocks all same size, and perfectly aligned on top of each other Has perfect perception of world (no ambiguities) 3 GOFAI (Good Old Fashioned AI) B C A A B C Initial situation Goal situation Standard type of problem in microworld Transform stacks of blocks in left scene to stack of blocks in right scene Input to planner might be goal situation (and (on A B) (on B C)) Not the geometrical description that humans see/use Representations chosen are usually highly dependent on the problem to be solved Often constrains the types of problems to work on 4 2

3 GOFAI (Good Old Fashioned AI) Large parts of AI devoted to recasting problems of intelligence in terms similar to simple blocks world descriptions Then finding ways to solve them This has not scaled well Microworlds were used to simplify the study of intelligence to manageable levels Implicitly assumes that intelligence is about problem solving (recall Minsky) The essence of intelligence in solving a puzzle omits lots of details not important to explicit statement of the puzzle 5 An Alternate View Intelligence is all about making judgments when there are large numbers of messy details all around Especially when no clear single best answer Analogy to Sherlock Holmes His strength was perceiving details others did not notice Perception was not abstract and distant At scene of crime, walked along getaway lanes, poked his head into the pantry Directly experienced what was there and where 6 3

4 From an Evolutionary Perspective Consider evolutionary timescale of 4.6 billion years? Single cell entities arose roughly 3.5 billion years ago A billion years passed before photosynthetic plants Fish and vertebrates arrived around 550 million years ago Insects at 450 million years ago Slow start Things now move fast Reptiles appeared 370 million years ago Dinosaurs at 330 million years ago Mammals at 250 million years ago First primates appeared 120 million years ago Predecessors of great apes at 18 million years ago Humankind around 2.5 million years ago Agriculture (19,000), writing (5,000), scientific knowledge (last few hundred years) 7 Another Approach to Intelligence Consider the previous evolutionary perspective Stick with messy detailed world Consider how a creature might get around and survive in the world Evolution spent most of its time in this before getting to us 8 4

5 New AI: Embodied Intelligence Key points Intelligent systems operate in a world that is Complex, uncertain, and not fully perceivable It carries out tasks involving perception and motion Must do some things to survive in world Not do solve problems built into it by researchers Artificial creature is embodied Effects of actions depend on state of external world External world influences perception of creature It must have internal drive to direct operations in world Hunger for electricity Reductionist approach to AI Shift from solving problems to existing within a world and maintaining of goals 9 Embodied AI vs. GOFAI Task Complexity Traditional AI starting region Target Embodied AI starting region Environment Complexity Traditional AI research keeps task difficulty Then tries to make environment complex Embodied AI research starts with most complex world ever to be encountered Then takes up challenge of task to perform in that environment 10 5

6 Braitenberg s Vehicles Valentino Braitenberg Neuroscientist Wrote Vehicles: Experiments in Synthetic Psychology (1984) Series of fourteen thought experiments about building little vehicles to operate in the world Relates physical systems to concepts of psychology, cognition, and free will Each chapter discusses a different vehicle Increasing in sophistication Display increasingly life-like phenomena Eventually exhibit behavior like egotism and optimism 11 The Vehicles Braitenberg s Vehicle 1 One sensor connected to one actuator More stuff sensed makes it go faster Suppose Sensor measures temperature Actuator is little rocket engine, with force proportional to temperature Result In friction environment Go faster when warm, and slower when cold Can veer off straight path due to non-smooth world In frictionless environment (outer space) Vehicle never slows down (acceleration proportional to temperature) Proceeds in straight line Sensor Actuator 12 6

7 Moral 1: Situatedness The behavior of a vehicle, or creature, depends on the environment in which it is embedded or situated 13 The Vehicles Braitenberg s Vehicle 2 Design Two actuators in laterally symmetric position Two sensors facing forward, symmetrically placed Comes in a number of types Depending on how sensors are connected to actuators When actuators apply same force, go straight ahead When right actuator apply more force than the left, vehicle veers left

8 The Vehicles Vehicle 2.a Left sensor is connected to left actuator Right sensor connected to right actuator Suppose Sensors measure intensity of light coming from source Actuators produce more force (speed) when there is higher light intensity level The Vehicles Result of Vehicle 2.a If right sensor closer to light source than left sensor Right sensor gets more light Then right actuator drives harder making it turn left and away from light source Initial turning cause even more turning 16 8

9 The Vehicles Vehicle 2.b Crossed sensor/actuator connections Left sensor is connected to right actuator Right sensor connected to left actuator When light falling on right sensor more than falling on left sensor Left actuator produces higher force Causes vehicle to turn to right Towards the light As get closer to light, both actuators increase and vehicle accelerate toward light Eventually running right into it 17 Moral 2: Embodiment The actions of a creature are part of a dynamic with the world and have immediate feedback on the creature s own sensations through direct physical coupling and its consequences 18 9

10 Vehicle Behaviors Braitenberg describes vehicle behaviors in anthropomorphic terms From point of view of an observer Vehicles 2a and 2b both seem to dislike light sources Vehicle 2a is a coward (moves away from light source) Vehicle 2b is aggressive toward light sources (smashing into them at high velocity) There is nothing about like, dislike, or aggression explicitly built into the vehicles But observers do describe the behavior of the vehicles in those terms! 19 Moral 3 Terms descriptive of behavior are in the eye of the observer 20 10

11 Other Vehicles Vehicles 3.a and 3.b Sensors inhibit the actuator The more of the sensed quantity, less force produced by actuator These vehicles slow down in vicinity of light source Steering is opposite to class 2 counterparts Vehicle 3.a (uncrossed wires) Tends to stay centered on light source until stop in front of light Vehicle 3.b (crossed wires) Tends to veer away from light source When eventually faces away from light source, then more influenced by rest of environment Once inhibition added to connection options, opens up possibility to build more behaviors 21 Other Vehicles Vehicles 4.a Adds non-linear relationships to wires connecting sensors and actuators Leads to very complicated behavior in all sorts of environments But too complex to describe the behavior of the vehicles directly Instead use descriptive terms like instinct to describe particular action patterns The resulting behavior is generated by the totality of the system Not by any one piece 22 11

12 Moral 4: Emergence The intelligence of the system emerges from the system s interactions with the world and from sometimes indirect interactions between its components it is sometimes hard to point to one event or place within the system and say that is why some external action was manifested 23 Emergence Emergence is not a linear phenomena Behavior produced by the system is more than the sum of its parts We do not necessarily need to build an explicit behavior into the system itself More with vehicles In Vehicle 4.b, discontinuous connecting elements introduce thresholds into system Vehicles appear to reach decisions at times In Vehicle class 5, networks of non-linear elements are introduced Vehicles now have memory (more on this later ) 24 12

13 Vehicles as Creatures Braitenberg devised artificial creatures (called vehicles) that live in a world Braitenberg did not simplify the world to be clean and neat Instead he describes how properties of world may effect the behavior of his creations Starting from very simple creatures, Braitenberg sketches out how one could proceed in bottom-up manner to reach intelligence 25 Autonomy An autonomous vehicle/creature Able to maintain long-term dynamic with environment without intervention Once switched on, it does what is in its nature to do 26 13

14 Physical Artificial Creatures Tortoises Elmer and Elsie (1950) W. Grey Walter Design Two electric motor actuators Single bump sensor Light sensor Recharging hutch Explored environment with complex behaviors Remarkably unpredictable 27 From 1950 SciAm Article Seeking light Reaction to obstacle 28 14

15 Physical Artificial Creatures MIT 29 Other Robots 30 15

16 Physical Non-Artificial Creatures Are artificial creature behaviors anything (computationally) like real creatures? Animals Frogs Bug detector : respond to size and motion Pigeons Navigation: stars, sun, magnetic fields, olfactory, ultrasound Ranked sensors (layered), not sensor fusion Jumping spiders Responds to moving stimulus (prey, courtship) Bees Dance language for signaling location of food to others Seem to be implementable for artificial creatures 31 Summary Compared mainstream AI to early artificial creature approaches GOFAI Braitenberg s vehicles Looked at some simple animals to see how they operate in their worlds Making comparisons to artificial creatures 32 16