CS 460, Lecture 2Last Time: Acting Humanly: The Full Turing Test• Alan Turing's 1950 article Computing Machinery and Intelligencediscussed conditions for considering a machine to be intelligent• “Can machines think?” ←→ “Can machines behave intelligently?”• The Turing test (The Imitation Game): Operational definition of intelligence.• Computer needs to possess: Natural language processing, Knowledge representation, Automated reasoning, and Machine learning•Problem:1) Turing test is not reproducible, constructive, and amenable to mathematic analysis. 2) What about physical interaction with interrogator and environment?• Total Turing Test: Requires physical interaction and needs perception and actuation.CS 460, Lecture 2Last time: The Turing Testhttp://www.ai.mit.edu/projects/infolab/http://aimovie.warnerbros.comCS 460, Lecture 2Last time: The Turing Testhttp://www.ai.mit.edu/projects/infolab/http://aimovie.warnerbros.comCS 460, Lecture 2Last time: The Turing Testhttp://www.ai.mit.edu/projects/infolab/http://aimovie.warnerbros.comCS 460, Lecture 2Last time: The Turing Testhttp://www.ai.mit.edu/projects/infolab/http://aimovie.warnerbros.comCS 460, Lecture 2Last time: The Turing Testhttp://www.ai.mit.edu/projects/infolab/http://aimovie.warnerbros.comCS 460, Lecture 2This time: Outline• Intelligent Agents (IA)• Environment types• IA Behavior•IA Structure•IA TypesCS 460, Lecture 2What is an (Intelligent) Agent?• An over-used, over-loaded, and misused term.• Anything that can be viewed asperceiving its environment through sensors and acting upon that environment through its effectors to maximize progress towards its goals.CS 460, Lecture 2What is an (Intelligent) Agent?• PAGE (Percepts, Actions, Goals, Environment)• Task-specific & specialized: well-defined goalsand environment• The notion of an agent is meant to be a tool for analyzing systems, • It is not a different hardware or new programming languagesCS 460, Lecture 2• Example: Human mind as network of thousands or millions of agents working in parallel. To produce real artificial intelligence, this school holds, we should build computer systems that also contain many agents and systems for arbitrating among the agents' competing results. • Distributed decision-making and control• Challenges:• Action selection: What next actionto choose• Conflict resolutionIntelligent Agents and Artificial IntelligencesensorseffectorsAgencyCS 460, Lecture 2Agent TypesWe can split agent research into two main strands:• Distributed Artificial Intelligence (DAI) –Multi-Agent Systems (MAS) (1980 – 1990)• Much broader notion of "agent" (1990’s – present)• interface, reactive, mobile, informationCS 460, Lecture 2Rational AgentsEnvironmentAgentperceptsactions?SensorsEffectorsHow to design this?CS 460, Lecture 2Remember: the Beobot exampleCS 460, Lecture 2A Windshield Wiper AgentHow do we design a agent that can wipe the windshieldswhen needed?•Goals? • Percepts?•Sensors?• Effectors?•Actions?• Environment?CS 460, Lecture 2A Windshield Wiper Agent (Cont’d)• Goals: Keep windshields clean & maintain visibility• Percepts: Raining, Dirty• Sensors: Camera (moist sensor)• Effectors: Wipers (left, right, back)• Actions: Off, Slow, Medium, Fast• Environment: Inner city, freeways, highways, weather …CS 460, Lecture 2Towards Autonomous Vehicleshttp://iLab.usc.eduhttp://beobots.orgCS 460, Lecture 2Interacting AgentsCollision Avoidance Agent (CAA)• Goals: Avoid running into obstacles•Percepts ?•Sensors?• Effectors ?•Actions ?• Environment: FreewayLane Keeping Agent (LKA)• Goals: Stay in current lane•Percepts ?•Sensors?• Effectors ?•Actions ?• Environment: FreewayCS 460, Lecture 2Interacting AgentsCollision Avoidance Agent (CAA)• Goals: Avoid running into obstacles• Percepts: Obstacle distance, velocity, trajectory• Sensors: Vision, proximity sensing• Effectors: Steering Wheel, Accelerator, Brakes, Horn, Headlights• Actions: Steer, speed up, brake, blow horn, signal (headlights)• Environment: Freeway Lane Keeping Agent (LKA)• Goals: Stay in current lane• Percepts: Lane center, lane boundaries• Sensors: Vision• Effectors: Steering Wheel, Accelerator, Brakes• Actions: Steer, speed up, brake• Environment: FreewayCS 460, Lecture 2Conflict Resolution by Action Selection Agents• Override: CAA overrides LKA• Arbitrate: if Obstacle is Close then CAAelse LKA• Compromise: Choose action that satisfies bothagents• Any combination of the above• Challenges: Doing the right thingCS 460, Lecture 2The Right Thing = The Rational Action• Rational Action: The action that maximizes the expected value of the performance measure given the percept sequence to date• Rational = Best ?• Rational = Optimal ?• Rational = Omniscience ? • Rational = Clairvoyant ?• Rational = Successful ?CS 460, Lecture 2The Right Thing = The Rational Action• Rational Action: The action that maximizes the expected value of the performance measure given the percept sequence to date• Rational = Best Yes, to the best of its knowledge• Rational = Optimal Yes, to the best of its abilities (incl.• Rational ≠ Omniscience its constraints)• Rational ≠ Clairvoyant • Rational ≠ SuccessfulCS 460, Lecture 2Behavior and performance of IAs• Perception (sequence) to Action Mapping:f : P* →A• Ideal mapping: specifies which actions an agent ought to take at any point in time• Description: Look-Up-Table, Closed Form, etc. • Performance measure: a subjectivemeasure to characterize how successful an agent is (e.g., speed, power usage, accuracy, money, etc.)• (degree of) Autonomy: to what extent is the agent able to make decisions and take actions on its own?CS 460, Lecture 2Look up tableagentobstaclesensorStop2Turn left 30 degrees5No action10ActionDistanceCS 460, Lecture 2Closed form• Output (degree of rotation) = F(distance)• E.g., F(d) = 10/d (distance cannot be less than 1/18)CS 460, Lecture 2How is an Agent different from other software?•Agents are autonomous, that is, they act on behalf of the user • Agents contain some level of intelligence, from fixed rules to learning engines that allow them to adapt to changes in the environment• Agents don't only act reactively, but sometimes also proactivelyCS 460, Lecture 2How is an Agent different from other software?• Agents have social ability,