Ai class
- 1. Artificial Intelligence Instructor: Monica Nicolescu
- 2. Artificial Intelligence 2 Outline Introduction – Robotics: what it is, what it isn’t, and where it came from – Key concepts • Brief history • Robot control architectures – Deliberative control – Reactive control – Hybrid control – Behavior-based control
- 3. Artificial Intelligence 3 Key Concepts • Situatedness – Agents are strongly affected by the environment and deal with its immediate demands (not its abstract models) directly • Embodiment – Agents have bodies, are strongly constrained by those bodies, and experience the world through those bodies, which have a dynamic with the environment
- 4. Artificial Intelligence 4 Key Concepts (cont.) • Situated intelligence – is an observed property, not necessarily internal to the agent or to a reasoning engine; instead it results from the dynamics of interaction of the agent and environment – and behavior are the result of many interactions within the system and w/ the environment, no central source or attribution is possible
- 5. Artificial Intelligence 5 What is Robotics? • Robotics is the study of robots, autonomous embodied systems interacting with the physical world • A robot is an autonomous system which exists in the physical world, can sense its environment and can act on it to achieve some goals • Robotics addresses perception, interaction and action, in the physical world
- 6. Artificial Intelligence 6 Uncertainty • Uncertainty is a key property of existence in the physical world • Physical sensors provide limited, noisy, and inaccurate information • Physical effectors produce limited, noisy, and inaccurate action • The uncertainty of physical sensors and effectors is not well characterized, so robots have no available a priori models
- 7. Artificial Intelligence 7 Uncertainty (cont.) • A robot cannot accurately know the answers to the following: – Where am I? – Where are my body parts, are they working, what are they doing? – What did I just do? – What will happen if I do X? – Who/what are you, where are you, what are you doing, etc.?...
- 8. Artificial Intelligence 8 The term “robot” • Karel Capek’s 1921 play RUR (Rossum’s Universal Robots) • It is (most likely) a combination of “rabota” (obligatory work) and “robotnik” (serf) • Most real-world robots today do perform such “obligatory work” in highly controlled environments – Factory automation (car assembly) • But that is not what robotics research about; the trends and the future look much more interesting
- 9. Artificial Intelligence 9 Classical activity decomposition • Locomotion (moving around, going places) – factory delivery, Mars Pathfinder, lawnmowers, vacuum cleaners... • Manipulation (handling objects) – factory automation, automated surgery... • This divides robotics into two basic areas – mobile robotics – manipulator robotics • … but these are merging in domains like robot pets, robot soccer, and humanoids
- 10. Artificial Intelligence 10 An assortment of robots…
- 11. Artificial Intelligence 11 Anthropomorphic Robots
- 12. Artificial Intelligence 12 Animal-like Robots
- 13. Artificial Intelligence 13 Humanoid Robots Robonaut (NASA) Sony Dream Robot Asimo (Honda) DB (ATR) QRIO
- 14. Artificial Intelligence 14 Outline • Introduction – Robotics: what it is, what it isn’t, and where it came from – Key concepts Brief history • Robot control architectures – Deliberative control – Reactive control – Hybrid control – Behavior-based control
- 15. Artificial Intelligence 15 A Brief History of Robotics • Robotics grew out of the fields of control theory, cybernetics and AI • Robotics, in the modern sense, can be considered to have started around the time of cybernetics (1940s) • Early AI had a strong impact on how it evolved (1950s-1970s), emphasizing reasoning and abstraction, removal from direct situatedness and embodiment • In the 1980s a new set of methods was introduced and robots were put back into the physical world
- 16. Artificial Intelligence 16 Cybernetics • Pioneered by Norbert Wiener in the 1940s • Combines principles of control theory, information science and biology • Sought principles common to animals and machines, especially with regards to control and communication • Studied the coupling between an organism and its environment
- 17. Artificial Intelligence 17 W. Grey Walter’s Tortoise • Machina Speculatrix” (1953) – 1 photocell, 1 bump sensor, 1 motor, 3 wheels, 1 battery, analog circuits • Behaviors: – seek light – head toward moderate light – back from bright light – turn and push – recharge battery • Uses reactive control, with behavior prioritization
- 18. Artificial Intelligence 18 Braitenberg Vehicles • Valentino Braitenberg (1980) • Thought experiments – Use direct coupling between sensors and motors – Simple robots (“vehicles”) produce complex behaviors that appear very animal, life-like • Excitatory connection – The stronger the sensory input, the stronger the motor output – Light sensor → wheel: photophilic robot (loves the light) • Inhibitory connection – The stronger the sensory input, the weaker the motor output – Light sensor → wheel: photophobic robot (afraid of the light)
- 19. Artificial Intelligence 19 Example Vehicles • Wide range of vehicles can be designed, by changing the connections and their strength • Vehicle 1: – One motor, one sensor • Vehicle 2: – Two motors, two sensors – Excitatory connections • Vehicle 3: – Two motors, two sensors – Inhibitory connections Being “ALIVE” “FEAR” and “AGGRESSION” “LOVE” Vehicle 1 Vehicle 2
- 20. Artificial Intelligence 20 Artificial Intelligence • Officially born in 1956 at Dartmouth University – Marvin Minsky, John McCarthy, Herbert Simon • Intelligence in machines – Internal models of the world – Search through possible solutions – Plan to solve problems – Symbolic representation of information – Hierarchical system organization – Sequential program execution
- 21. Artificial Intelligence 21 AI and Robotics • AI influence to robotics: – Knowledge and knowledge representation are central to intelligence • Perception and action are more central to robotics • New solutions developed: behavior-based systems – “Planning is just a way of avoiding figuring out what to do next” (Rodney Brooks, 1987) • First robots were mostly influenced by AI (deliberative)
- 22. Artificial Intelligence 22 Outline • Introduction – Robotics: what it is, what it isn’t, and where it came from – Key concepts • Brief history Robot control architectures – Deliberative control – Reactive control – Hybrid control – Behavior-based control
- 23. Artificial Intelligence 23 Control Architecture • A robot control architecture provides the guiding principles for organizing a robot’s control system • It allows the designer to produce the desired overall behavior • The term architecture is used similarly as “computer architecture” – Set of principles for designing computers from a collection of well-understood building blocks • The building-blocks in robotics are dependent on the underlying control architecture
- 24. Artificial Intelligence 24 Robot Control • Robot control is the means by which the sensing and action of a robot are coordinated • There are infinitely many ways to program a robot, but there are only few types of robot control: – Deliberative control – Reactive control – Hybrid control – Behavior-based control
- 25. Artificial Intelligence 25 Spectrum of robot control From “Behavior-Based Robotics” by R. Arkin, MIT Press, 1998
- 26. Artificial Intelligence 26 Thinking vs. Acting • Thinking/Deliberating – involves planning (looking into the future) to avoid bad solutions – flexible for increasing complexity – slow, speed decreases with complexity – thinking too long may be dangerous – requires (a lot of) accurate information • Acting/Reaction – fast, regardless of complexity – innate/built-in or learned (from looking into the past) – limited flexibility for increasing complexity
- 27. Artificial Intelligence 27 Robot control approaches • Reactive Control – Don’t think, (re)act. • Deliberative (Planner-based) Control – Think hard, act later. • Hybrid Control – Think and act separately & concurrently. • Behavior-Based Control (BBC) – Think the way you act.
- 28. Artificial Intelligence 28 A Brief History • Deliberative Control (late 70s) • Reactive Control (mid 80s) – Subsumption Architecture (Rodney Brooks) • Behavior-Based Systems (late 80s) • Hybrid Systems (late 80s/early 90s)
- 29. Artificial Intelligence 29 Outline • Introduction – Robotics: what it is, what it isn’t, and where it came from – Key concepts • Brief history • Robot control architectures Deliberative control – Reactive control – Hybrid control – Behavior-based control
- 30. Artificial Intelligence 30 Deliberative Control: Think hard, then act! • In DC the robot uses all the available sensory information and stored internal knowledge to create a plan of action: sense → plan → act (SPA) paradigm • Limitations – Planning requires search through potentially all possible plans ⇒ these take a long time – Requires a world model, which may become outdated – Too slow for real-time response • Advantages – Capable of learning and prediction – Finds strategic solutions
- 31. Artificial Intelligence 31 Early AI Robots • Shakey (1960, Stanford Research Institute) • Stanford Cart (1977) and CMU rover (1983) • Interpreting the structure of the environment from visual input involved complex processing and required a lot of deliberation • Used state-of-the-art computer vision techniques to provide input to a planner and decide what to do next (how to move)
- 32. Artificial Intelligence 32 Outline • Introduction – Robotics: what it is, what it isn’t, and where it came from – Key concepts • Brief history • Robot control architectures – Deliberative control Reactive control – Hybrid control – Behavior-based control
- 33. Artificial Intelligence 33 Reactive Control: Don’t think, react! • Technique for tightly coupling perception and action to provide fast responses to changing, unstructured environments • Collection of stimulus-response rules • Limitations – No/minimal state – No memory – No internal representations of the world – Unable to plan ahead • Advantages – Very fast and reactive – Powerful method: animals are largely reactive
- 34. Artificial Intelligence 34 Vertical v. Horizontal Systems Traditional (SPA): sense – plan – act Subsumption: (Rodney Brooks) “The world is its own best model.”
- 35. Artificial Intelligence 35 The Subsumption Architecture • Principles of design – systems are built incrementally – components are task-achieving actions/behaviors (avoid-obstacles, find-doors, visit-rooms) – all rules can be executed in parallel, not in a sequence – components are organized in layers, from the bottom up – lowest layers handle most basic tasks – newly added components and layers exploit the existing ones
- 36. Artificial Intelligence 36 Subsumption Layers • First, we design, implement and debug layer 0 • Next, we design layer 1 – When layer 1 is designed, layer 0 is taken into consideration and utilized, its existence is subsumed – Layer 0 continues to function • Continue designing layers, until the desired task is achieved • Higher levels can – Inhibit outputs of lower levels – Suppress inputs of lower levels level 2 level 1 level 0 sensors actuators AFSMinputs outputs suppressor inhibitor I s
- 37. Artificial Intelligence 37 Subsumption Architecture Validation • Practically demonstrated on navigation, 6-legged walking, chasing, soda-can collection, etc.
- 38. Artificial Intelligence 38 Outline • Introduction – Robotics: what it is, what it isn’t, and where it came from – Key concepts • Brief history • Robot control architectures – Deliberative control – Reactive control Hybrid control – Behavior-based control
- 39. Artificial Intelligence 39 Hybrid Control: Think and act independently & concurrently! • Combination of reactive and deliberative control – Reactive layer (bottom): deals with immediate reaction – Deliberative layer (top): creates plans – Middle layer: connects the two layers • Usually called “three-layer systems” • Major challenge: design of the middle layer – Reactive and deliberative layers operate on very different time-scales and representations (signals vs. symbols) – These layers must operate concurrently • Currently one of the two dominant control paradigms in robotics
- 40. Artificial Intelligence 40 Reaction – Deliberation Coordination • Selection: Planning is viewed as configuration • Advising: Planning is viewed as advice giving • Adaptation: Planning is viewed as adaptation • Postponing: Planning is viewed as a least commitment process Flakey TJ
- 41. Artificial Intelligence 41 Outline • Introduction – Robotics: what it is, what it isn’t, and where it came from – Key concepts • Brief history • Robot control architectures – Deliberative control – Reactive control – Hybrid control Behavior-based control
- 42. Artificial Intelligence 42 Behavior-Based Control Think the way you act! • An alternative to hybrid control, inspired from biology • Behavior-based control involves the use of “behaviors” as modules for control • Historically grew out of reactive systems, but not constrained • Has the same expressiveness properties as hybrid control • The key difference is in the “deliberative” component
- 43. Artificial Intelligence 43 What Is a Behavior? Rules of implementation • Behaviors achieve or maintain particular goals (homing, wall-following) • Behaviors are time-extended processes • Behaviors take inputs from sensors and from other behaviors and send outputs to actuators and other behaviors • Behaviors are more complex than actions (stop, turn- right vs. follow-target, hide-from-light, find-mate etc.)
- 44. Artificial Intelligence 44 Principles of BBC Design • Behaviors are executed in parallel, concurrently – Ability to react in real-time • Networks of behaviors can store state (history), construct world models/representation and look into the future – Use representations to generate efficient behavior • Behaviors operate on compatible time-scales – Ability to use a uniform structure and representation throughout the system
- 45. Artificial Intelligence 45 Behavior Coordination • Behavior-based systems require consistent coordination between the component behaviors for conflict resolution • Coordination of behaviors can be: – Competitive: one behavior’s output is selected from multiple candidates – Cooperative: blend the output of multiple behaviors – Combination of the above two
- 46. Artificial Intelligence 46 Competitive Coordination • Arbitration: winner-take-all strategy ⇒ only one response chosen • Behavioral prioritization – Subsumption Architecture • Action selection/activation spreading (Pattie Maes) – Behaviors actively compete with each other – Each behavior has an activation level driven by the robot’s goals and sensory information • Voting strategies – Behaviors cast votes on potential responses
- 47. Artificial Intelligence 47 Cooperative Coordination • Fusion: concurrently use the output of multiple behaviors • Major difficulty in finding a uniform command representation amenable to fusion • Fuzzy methods • Formal methods – Potential fields – Motor schemas – Dynamical systems
- 48. Artificial Intelligence 48 Fusion: flocking (formations) Example of Behavior Coordination Arbitration: foraging (search, coverage)
- 49. Artificial Intelligence 49 Example of representation • A network of behaviors representing spatial landmarks, used for path planning by message- passing (Matarić 90)
- 50. Artificial Intelligence 50 Behavior-Based Control summary • Alternative to hybrid systems; encourages uniform time-scale and representation throughout the system • Scalable and robust • Behaviors are reusable; behavior libraries • Facilitates learning • Requires a clever means of distributing representation and any potentially time-extended computation
- 51. Artificial Intelligence 51 Robotics Challenges • Perception – Limited, noisy sensors • Actuation – Limited capabilities of robot effectors • Thinking – Time consuming in large state spaces • Environments – Dynamic, impose fast reaction times
- 52. Artificial Intelligence 52 Lessons Learned • Move faster, more robustly • Think in such a way as to allow this action • New types of robot control: – Reactive, hybrid, behavior-based • Control theory – Continues to thrive in numerous applications • Cybernetics – Biologically inspired robot control • AI – Non-physical, “disembodied thinking”
- 53. Artificial Intelligence 53 Background Readings • Ronald Arkin, “Behavior- Based Robotics”, 2001.