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INTELLIGENCE WITHOUT REPRESENTATION Overview 2 (1) (2) (3) (4) (5) (6) (7) (8) Definitions and introduction Adoptions from evolution Abstraction and intelligence Building creatures Compensation for representation The creatures Comparison to other approaches Limitations and conclusions The presented paper 3 Intelligence without representation Rodney A. Brooks, 1987 describes research done at the MIT Artificial Intelligence Laboratory research is supported by IBM Faculty 9 Development and others career summary current professor at the MIT former director of CSAIL, founder of iRobot Corp. 4 DEFINITIONS AND INTRODCTION Intelligence without representation 5 Hard to give an exact definition The sum of higher cognitive abilities abstract thinking building representations problem solving reasoning (Sternberg & Berg) Popular consent for human Intelligence Intelligence without representation 6 Artificial intelligence tries to replicate human intelligence naive goal of research, very large area to work Today AI is often divided into smaller subareas represent knowledge natural language understanding computer vision planning etc. relevant AI domains navigation medical diagnosis scheduling etc. Intelligence without representation 7 Etymology human substitution for someone not present In AI symbols build a representation each symbol holds information the symbol is interpreted beyond its object character (likewise a variable) an internal model of the world is build (Strube) One early AI example: Blocks world Intelligence without representation 8 AI System: Blocks world logical planning to achieve the goal most important: an explicit representation of the state of the world Intelligence without representation 9 AI System: Blocks world one possible representation of the initial state On(A, table) ^ On(B, table) ^ On(C, table) Intelligence without representation 10 On(A, table) ^ On(B, table) ^ On(C, table) Brooks criticism This describes not the underlying concept on the whole e.g. the distance between blocks, size of blocks etc. Humans representations are much too complex to replicate them in detail Intelligence without representation 11 The main idea (roughly) by reacting on the real world, representations are not necessary produce intelligent systems which include perception and action as well Intelligence without representation 12 2 steps to achieve this 2. Build intelligent systems incrementally, ensure that every step is a complete and valid system Each System must work in the real world This forces one to start with very simple systems 1. 13 ADOPTIONS FROM EVOLUTION Adoptions from evolution 14 Evolution already created intelligence Once the “essence of being and reacting” is established, higher abilities are simple to achieve Adoptions from evolution 15 Starting with a simple system Robot needs mobility, vision and survival related tasks to have a basis for intelligence 16 ABSTRACTION AND INTELLIGENCE Abstraction and intelligence 17 Brooks argues that AI suffers from abstraction Unsolved subproblems of AI are considered as non-AI problems Perception or motor skills are often excluded They are part of AI as well and limit the remaining abilities to a high degree A system cannot reason beyond its representation Abstraction and intelligence 18 A system cannot reason beyond its representation Example MYCIN good diagnosis system for bacterial infections has no model of the human body if the aorta is ruptured and the patient looses blood every second MYCIN will search for bacterial causes Each robot needs its own “Merkwelt” Merkwelt 19 a concept of J. J. Uexküll, who was a popular German biologist „Einen anderen Standpunkt gegenüber dem Weltpanorama als den unseres Subjektes gib es nicht, weil das Subjekt als Beschauer zugleich der Erbauer seiner Welt ist. Ein objektives Weltbild, das allen Subjekten gerecht werden soll, muss notwendig ein Phantom bleiben.“ (Uexküll) Merkwelt 20 Our model of the world is determined by the things we are able to sense We miss many things according to our anatomy We should not force our Merkwelt on the robot Instead, the robot must build its own Merkwelt 21 BUILDING CREATURES Building a creature 22 Brooks wishes to create entities that must... 1. 2. 3. 4. deal with their environment deal with changes in their environment be able to maintain several goals do something in the world, have a purpose in being How to build such creatures? Which methodology? Building a creature 23 Engineering methodology for building creatures 1. 2. 3. decompose the system into parts build the parts interface them into a complete system Decompose by function? every function gets its module (eg. vision) Decompose by action? every output action gets its own layer Decomposition by function 24 a vision module perceives an image a central system processes the data a motor system controls the output a chain of modules is needed from input to output every module needs special interfaces almost not possible to... locate errors in a long chain model complex behaviour Decomposition by action 25 each layer is a “activity producing system” layers connect input to output sensing to action layers can interact with other layers massively parallel working agent each layer implements one activity Decomposition by action 26 Advantages of decomposition by action one can start with a simple but complete system for additional behaviour, new layers can be added intelligence is therefore incrementally raised an approach comparable to evolutionary processes Decomposition by action 27 Advantages of decomposition by action one can start with a simple but complete system for additional behaviour, new layers can be added intelligence is therefore incrementally raised an approach comparable to evolutionary processes Layers work in parallel and can be composed incrementally, Brooks calls this concept “subsumption architecture” 28 COMPENSATION FOR REPRESENTATION Compensation for representation 29 Is there a central representation? • Sensory input is not stored • Processes doing the perception are separated • Data is processed differently and independently There is no central representation • This occurred by accident Compensation for representation 30 Layer consists of several finite-state machines They have state, transition and action These are the modules doing the computation Their behavior/activity is depending on their state Example: states while parsing the word “nice” Compensation for representation 31 • • • The finite-state machines react to their input They cannot be controlled by a single control center Only new data-input result in a different state Although the states are data depended, they are not used as a representation to work with 32 THE CREATURES The creatures 33 Three kinds of robots: 1. Allen 2. Tom and Jerry 3. Herbert (from left to right) The creatures 34 Allen: • Allen is using an offboard LISP machine for computation support • Connections between the finite state machines are virtual wires The creatures 35 Tom and Jerry: • Maximum of 1Bit messages • The finite state machines are connected by real wires The creatures 36 Herbert: • Consists of 24 processors • Has an arm to grip cans • Maximum of 24Bit messages • The finite state machines are connected by real wires • Has infrared proximity sensors to avoid obstacles • Grasping is controlled by depth-sensor Architecture 37 The layer-system (subsumption architecture): • 3 layers were implemented • The first is most basic avoid • The second is to cause action and avoid standing still wander • The third uses lower level finite-state machines to explore explore Architecture 38 Architecture 39 The first layer Avoid • Makes the agent avoid hitting objects • Both static and moving objects are avoided • It is divided into several finite-state machines Architecture 40 Finite-state machines of Avoid Sonar • Runs sonar-system and emits every second a map of polar coordinates to 'Collide' and 'Feelforce' Architecture 41 Collide • Is looking for anything dead ahead, if so sends a halt to ‘Forward’ Feelforce (runs simultaneously to 'Collide'): • Each sonar return is considered as repulsive object causing this finite state machine to calculate a repulsive force • All repulsions are put together to produce the output given to 'Runaway' Architecture 42 Runaway • Simply thresholds the output from 'Feelforce' and passes it on to 'Turn' Turn • Directs the robot away from repulsive objects Forward • Drives the robot forwards • If the robot is moving forward and a halt is received it will stop moving forwards Architecture 43 The second layer Wander • When ‘turn’ and ‘forward’ have not the state ‘busy’ • Wander shall prevent the robot from standing still Architecture 44 Finite-state machines of Wander Wander • Generates a random heading every ten seconds • The heading is passed to the 'Avoid' machine Architecture 45 Avoid • Treats heading as attractive objects and sums it up with the map from 'Feelforce‘ • By injecting its results to the output of lower-level layers 'Runaway' it suppresses the lower-level behavior. • Thus the decision by 'Wander' is considered as well as the initial repulsive input • The input from 'Avoid' will be ignored if 'Turn' and 'Forward' are currently busy running the robot Architecture 46 The third layer Explore • Makes the robot try to explore • Interacting with the 'Wander'-layer Architecture 47 Finite-state machines of Wander Whenlook • Notices when the robot is not busy('Status') • Inhibits the machine 'Wander‘ • Starts the mechanism to look around for a distant location to explore ('Look'/'Stereo') Architecture 48 Stereo • Finds a location to explore • Sends output to 'Pathplan' and 'Integrate' Pathplan • Sends a direction to the machine 'Avoid'. This is to make lower-level avoidance still possible Integrate • As avoiding might corrupt the indicated direction given by 'Pathplan' this machine monitors the actual direction of the robot('Status') • It updates 'Pathplan' with the actual direction 49 COMPARISON TO OTHER APPROACHES Comparison to Connectionism 50 • • • • • Connectionism consists of many simple processing units They are highly linked Knowledge is stored in • the network structure • weighted links • special properties of units This can be considered as representation The processing units work in parallel Comparison to Connectionism 51 • • • The used finite-state machines are neither simple nor uniform They are only a little linked The developers are not interested in stored knowledge Comparison to Neural Networks 52 • • • • Neural Networks process activity They try to replicate human biology This contradicts strongly with Brooks idea of incremental intelligence Systems should not be copied on the whole Comparison to Production Rules 53 • • • • Consider the layers as production rules Each layer/production is selected according to the input But: there is no big rule database The concept is not based on selecting the adequate rule Comparison to Blackboard 54 Blackboard is an architecture with many independent modules • The modules write all information into one data structure Data is public and anonym • • • Brooks has no such information-repository The finite-state machines are in order so the information producer is determined Comparison to Uexküll 55 • • • • • Uexküll and Heidegger examined the dynamics of existence Merkwelt etc. Brooks finds many similarities in their approaches They occurred by accident, because Brooks only followed engineering considerations He designed the architecture on his own ideas Comparison to Other Approaches 56 • • There are many analogies between the architectures At least, Brooks finds some differences to all of them 57 LIMITATIONS Limitations – Number of layers 58 The number of layers will increase complexity • Three layers were tested with robots • Six layers were tested in simulation • Adding layers should only be done when the system runs without problems in real world • Because of the developed subsumption architecture, adding new layers is quite simple Limitations – Complexity 59 The number of layers will increase complexity • Brooks started of with a three layer system • A fourteen layer architecture is currently under development • Goal of this high-complex architecture is to wander around and take soda-cans to a bin • The complexity will always be lower than connectionist models Limitations - Learning 60 The number of layers will increase complexity • Learning is currently performed by an isolated module in order to copy the behavior of insects • The copied insects show a constant amount of knowledge • The module is not connected to other modules • Research is still in progress on how to connect them Conclusion 61 Conclusion and hypotheses of Brooks Representations are counterproductive for simple level intelligence. Instead, one should use the world as its own model (conclusion) Representations are the wrong unit of abstraction for intelligent systems (hypothesis) Conclusion 62 Replicating human intelligence leads to disregarding the basis of intelligence A traditional AI System is bound to the validity of its representation Brooks pursues his ways of achieving intelligence incrementally Outlook 63 • • • • Brooks lead to new approach in AI research Behaviour based artificial intelligence or situated AI His approach is often used in RoboCup-robots His foundation iRobot delivers many robots today • (PackBot at for rescuing) Outlook 64 • • • • Brooks lead to new approach in AI research Behaviour based artificial intelligence or situated AI His approach is often used in RoboCup-robots His foundation iRobot delivers many robots today • (Roomba for cleaning purposes) Criticism 65 • Because of the new and elementary approach, the evolution of such robots might start very slow (comparable to biological evolution) • No representation is used, what makes learning hardly possible • The interface-problems of “decomposition by function” cannot discard the whole principle • At least the state machines have a state and therefore some kind of representation