Artificial Intelligence

Artificial Intelligence Instructor: Monica Nicolescu

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 Artificial Intelligence

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 Artificial Intelligence

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 Artificial Intelligence

What is Robotics? • Robotics is the study of robots, autonomous embodied systems interacting with the physical world • A robot is an autonomoussystem which exists in the physical world, can senseits environment and canacton it to achieve some goals • Robotics addresses perception, interaction and action, in the physical world Artificial Intelligence

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 Artificial Intelligence

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.?... Artificial Intelligence

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 Artificial Intelligence

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 Artificial Intelligence

An assortment of robots… Artificial Intelligence

Anthropomorphic Robots Artificial Intelligence

Animal-like Robots Artificial Intelligence

Humanoid Robots QRIO Asimo (Honda) Artificial Intelligence DB (ATR) Robonaut (NASA) Sony Dream Robot

A Brief History of Robotics • Robotics grew out of the fields of control theory, cyberneticsandAI • 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 Artificial Intelligence

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 Artificial Intelligence

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 Artificial Intelligence

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) Artificial Intelligence

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 Vehicle 1 Being “ALIVE” “FEAR” and “AGGRESSION” Vehicle 2 “LOVE” Artificial Intelligence

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 Artificial Intelligence

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) Artificial Intelligence

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 Artificial Intelligence

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 Artificial Intelligence

Spectrum of robot control From “Behavior-Based Robotics” by R. Arkin, MIT Press, 1998 Artificial Intelligence

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 Artificial Intelligence

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. Artificial Intelligence

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) Artificial Intelligence

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 Artificial Intelligence

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) Artificial Intelligence

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 Reactive Control:Don’t think, react! Artificial Intelligence

Vertical v. Horizontal Systems Traditional (SPA): sense – plan – act Subsumption: (Rodney Brooks) “The world is its own best model.” Artificial Intelligence

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 Artificial Intelligence

inhibitor level 2 s inputs outputs level 1 AFSM I level 0 suppressor 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 sensors actuators Artificial Intelligence

Subsumption Architecture Validation • Practically demonstrated on navigation, 6-legged walking, chasing, soda-can collection, etc. Artificial Intelligence

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 Artificial Intelligence

Reaction – Deliberation Coordination Flakey • 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 TJ Artificial Intelligence

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 Artificial Intelligence

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.) Artificial Intelligence

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 Artificial Intelligence

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 Artificial Intelligence

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 Artificial Intelligence

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 Artificial Intelligence

Example of Behavior Coordination Fusion: flocking (formations) Arbitration:  foraging (search, coverage) Artificial Intelligence

Example of representation • A network of behaviors representing spatial landmarks, used for path planning by message-passing (Matarić 90) Artificial Intelligence

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 Artificial Intelligence

Artificial Intelligence