CMSC 671 Fall 2005Today’s classIntelligent AgentsHow do you design an intelligent agent?What do you mean, sensors/percepts and effectors/actions?A more specific example: Automated taxi driving systemRationalityAutonomySome agent types(0/1) Table-driven/reflex agent architecture(0) Table-driven agents(1) Simple reflex agents(2) Architecture for an agent with memory(2) Agents with memory(3) Architecture for goal-based agent(3) Goal-based agents(4) Architecture for a complete utility-based agent(4) Utility-based agentsProperties of EnvironmentsProperties of Environments IIProperties of Environments IIICharacteristics of environmentsSlide 24Slide 25Slide 26Slide 27Slide 28SummaryLisp RevisitedLisp basicsTypes of objectsBuilt-in functionsSpecial formsA Lisp exampleCMSC 671CMSC 671Fall 2005Fall 2005Class #2 – Tuesday, September 6Today’s class•What’s an agent?–Definition of an agent–Rationality and autonomy–Types of agents–Properties of environments•Lisp – a second lookIntelligent Intelligent AgentsAgentsChapter 2How do you design an intelligent agent?•Definition: An intelligent agent perceives its environment via sensors and acts rationally upon that environment with its effectors. •A discrete agent receives percepts one at a time, and maps this percept sequence to a sequence of discrete actions. •Properties –Autonomous –Reactive to the environment –Pro-active (goal-directed) –Interacts with other agents via the environmentWhat do you mean, sensors/percepts and effectors/actions?•Humans–Sensors: Eyes (vision), ears (hearing), skin (touch), tongue (gustation), nose (olfaction), neuromuscular system (proprioception)–Percepts: •At the lowest level – electrical signals from these sensors•After preprocessing – objects in the visual field (location, textures, colors, …), auditory streams (pitch, loudness, direction), …–Effectors: limbs, digits, eyes, tongue, …–Actions: lift a finger, turn left, walk, run, carry an object, …•The Point: percepts and actions need to be carefully defined, possibly at different levels of abstractionA more specific example: Automated taxi driving system•Percepts: Video, sonar, speedometer, odometer, engine sensors, keyboard input, microphone, GPS, …•Actions: Steer, accelerate, brake, horn, speak/display, …•Goals: Maintain safety, reach destination, maximize profits (fuel, tire wear), obey laws, provide passenger comfort, …•Environment: U.S. urban streets, freeways, traffic, pedestrians, weather, customers, …•Different aspects of driving may require different types of agent programs!Rationality•An ideal rational agent should, for each possible percept sequence, do whatever actions will maximize its expected performance measure based on (1) the percept sequence, and (2) its built-in and acquired knowledge. •Rationality includes information gathering, not “rational ignorance.” (If you don’t know something, find out!)•Rationality  Need a performance measure to say how well a task has been achieved.•Types of performance measures: false alarm (false positive) and false dismissal (false negative) rates, speed, resources required, effect on environment, etc.Autonomy•A system is autonomous to the extent that its own behavior is determined by its own experience.•Therefore, a system is not autonomous if it is guided by its designer according to a priori decisions.•To survive, agents must have: –Enough built-in knowledge to survive. –The ability to learn.Some agent types•(0) Table-driven agents –use a percept sequence/action table in memory to find the next action. They are implemented by a (large) lookup table. •(1) Simple reflex agents –are based on condition-action rules, implemented with an appropriate production system. They are stateless devices which do not have memory of past world states. •(2) Agents with memory –have internal state, which is used to keep track of past states of the world. •(3) Agents with goals –are agents that, in addition to state information, have goal information that describes desirable situations. Agents of this kind take future events into consideration. •(4) Utility-based agents –base their decisions on classic axiomatic utility theory in order to act rationally.(0/1) Table-driven/reflex agent architecture(0) Table-driven agents•Table lookup of percept-action pairs mapping from every possible perceived state to the optimal action for that state•Problems –Too big to generate and to store (Chess has about 10120 states, for example) –No knowledge of non-perceptual parts of the current state –Not adaptive to changes in the environment; requires entire table to be updated if changes occur –Looping: Can’t make actions conditional on previous actions/states(1) Simple reflex agents•Rule-based reasoning to map from percepts to optimal action; each rule handles a collection of perceived states•Problems –Still usually too big to generate and to store–Still no knowledge of non-perceptual parts of state –Still not adaptive to changes in the environment; requires collection of rules to be updated if changes occur –Still can’t make actions conditional on previous state(2) Architecture for an agent with memory(2) Agents with memory•Encode “internal state” of the world to remember the past as contained in earlier percepts.•Needed because sensors do not usually give the entire state of the world at each input, so perception of the environment is captured over time. “State” is used to encode different "world states" that generate the same immediate percept. •Requires ability to represent change in the world; one possibility is to represent just the latest state, but then can’t reason about hypothetical courses of action.•Example: Rodney Brooks’s Subsumption Architecture.(3) Architecture for goal-based agent(3) Goal-based agents•Choose actions so as to achieve a (given or computed) goal.•A goal is a description of a desirable situation.•Keeping track of the current state is often not enough  need to add goals to decide which situations are good •Deliberative instead of reactive.•May have to consider long sequences of possible actions before deciding if goal is achieved – involves consideration of the future, “what will happen if I do...?”(4) Architecture for a complete utility-based agent(4) Utility-based agents•When there are multiple possible alternatives, how