intelligent agent.pdf

In AI an agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through effectors/actuators. A human agent has eyes, ears, and other organs for sensors, and hands, legs, mouth, and other body parts for effectors. A robotic agent sub...

In AI an agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through effectors/actuators. A human agent has eyes, ears, and other organs for sensors, and hands, legs, mouth, and other body parts for effectors. A robotic agent substitutes cameras and infrared range finders for the sensors and various motors for the effectors. A software agent has encoded bit strings as its percepts and actions.  sensors percepts ? environment agent actions Figure 2 effectors  A rational agent is one that does the right thing. As a first approximation, we will say that the right action is the one that will cause the agent to be most successful. That leaves us with the problem of deciding and to evaluate the agent's success. There is a danger here for those who establish performance measures... If you measure success by the amount of dirt cleaned up, then some clever agent might choose to bring in a load of dirt each morning, quickly clean it up, and get a good performance score. What you really want to measure is how clean the floor is; but determining that is more difficult than just weighing the amount of dirt cleaned up. The when of evaluating performance is also important. If we measured how much dirt the agent had cleaned up in the first hour of the day, we would be rewarding some agents who start early (even if they do little or no work later on) and punishing those that work consistently. Thus, we want to measure performance over the long run, be it an eight-hour shift or a lifetime. NOTE: What is rational at any given point depends on the following: 1.The performance measure that defines degree of success. 2.Everything that the agent has perceived so far. This has to do with the complete perceptual history of the agent. This is called percept sequence. 3.What the agent knows about the environment. 4.The actions that the agent can perform.  This leads to the definition of an ideal rational agent: For each possible percept sequence, an ideal rational agent should do whatever action is expected to maximize its performance measure, based on the evidence provided by the percept sequence and whatever built-in knowledge the agent has.  Among other things, two important requirements that an intelligent agent ought to satisfy include: : the case of agents being designed based on both experience and built-in knowledge as against acting based on just the latter. “A system is autonomous to the extent that its behaviour is determined by its own experience”. Autonomy, when featured in systems, is an example of sound engineering practice. An agent that operates based on built-in assumptions will only operate successfully when prevailing assumptions hold, and thus lack flexibility. ENVIRONMENT TYPES/PROPERTIES Fully Observable vs. Partially Observable | Unobservable If an agent's sensory apparatus gives it access to the complete state of the environment, then we say that the environment is fully observable to that agent. An environment is effectively observable if the sensors detect all aspects that are relevant to the choice of action. A fully observable environment is convenient because the agent need not maintain any internal state to keep track of the world.  ENVIRONMENT PROPERTIES CONT’D Deterministic vs. Stochastic | Non-Deterministic. If the next state of the environment is completely determined by the current state and the actions selected by the agents, then we say the environment is deterministic. In principle, an agent need not worry about uncertainty in a fully observable, deterministic environment. If the environment is partially observable, however, then it may appear to be stochastic. This is particularly true if the environment is complex, making it hard to keep track of all the unobservable aspects. Thus, it is often better to think of an environment as deterministic or stochastic from the point of view of the agent.  ENVIRONMENT PROPERTIES CONT’D Episodic vs. Sequential. In an episodic environment, the agent's experience is divided into "episodes." Each episode consists of the agent perceiving and then acting. The quality of its action depends just on the episode itself, because subsequent episodes do not depend on what actions occur in previous episodes. Episodic environments are much simpler because the agent does not need to think ahead.  ENVIRONMENT PROPERTIES CONT’D Static vs. Dynamic. If the environment can change while an agent is deliberating, then we say the environment is dynamic for that agent; otherwise it is static. Static environments are easy to deal with because, nor need it worry about the passage the agent need not keep looking at the world while it is deciding on an action of time. If the environment does not change with the passage of time but the agent's performance score does, then we say the environment is semi-dynamic. ENVIRONMENT PROPERTIES CONT’D Discrete vs. Continuous. If there are a limited number of distinct, clearly defined percepts and actions we say that the environment is discrete. Chess is discrete – there are a fixed number of possible moves on each turn. Taxi driving is continuous – the speed and location of the taxi and the other vehicles sweep through a range of continuous values. ENVIRONMENT PROPERTIES CONT’D  The job of AI is to basically design the agent program with: a function that implements the agent mapping from to. an assumption that the program will run on some sort of computing device, called the.  The architecture might be a plain computer, or it might include a special-purpose hardware for certain tasks, such as processing camera images or filtering audio input.  The relationship among agents, architectures, and programs can be summed up as follows: 2. Before an agent program is designed, it is expected that one must have a good idea of the required or possible percepts and corresponding actions expected of the agent. 3. The goal(s) or performance measure(s) the agent is supposed to achieve, and 4. The sort of environment it will operate in.  AGENT TYPE PERFORMANCE ENVIRONMENT ACTUATORS SENSORS MEASURE Medical diagnostic Healthy patient, Patient, hospital, staff Display of questions, Keyboard entry of system minimize costs tests, treatments symptoms, findings, patient’s answers Satellite image analysis Correct image Images from orbiting Display of scene Pixels of varying intensity, system categorization satellite categorization colour Part-picking robot Percentage of parts Conveyor belt with Jointed arm and hand Camera, joint angle in correct bins parts, bins sensors Refinery controller Maximize purity, Refinery, operators Valves, pumps, heaters, Temperature, pressure yield, safety displays readings Interactive English Maximize student’s Set of students, testing Display of exercises, Keyboard entry tutor score on test agency suggestions, corrections Points to note about the basic structure of an agent: An agent can receive only a single percept as input at a time. In a simple situation, an agent may be successful without saving percept in memory. However, in a complex situation, an agent must store percepts in memory to attain a certain level of success, but it is not possible to store the complete percept sequence. Performance measure is but a yardstick for measuring the efficacy of an agent and not a part of the agent program. AGENT PROGRAMS SCENARIO – TAXI DRIVER AGENT SYSTEM Let’s look at the job of designing an automated taxi driver. The full driving task is extremely open- ended – there is no limit to the possible combinations of circumstances that can arise (which is another reason this example is chosen). for the taxi.  Let’s look at the job of designing an automated taxi driver. The full driving task is extremely open-ended – there is no limit to the possible combinations of circumstances that can arise (which is another reason this example is chosen). We must first think about the percepts, actions, goals and environment for the taxi. AGENT TYPE PERFORMANCE ENVIRONMENT ACTUATORS SENSORS MEASURES Taxi Driver AGENT TYPE PERFORMANCE ENVIRONMENT ACTUATORS SENSORS MEASURES Taxi Safe, Fast, Legal, Roads, Other Steer, accelerate, Cameras, Driver comfortable trip, traffic, brake, talk to Speedometer, GPS, Maximize profits Pedestrians, passenger SONAR, Customers Microphone, Keyboard The taxi will need to know where it is, what else is on the road, and how fast it is moving. This information can be obtained from the percepts provided by one or more controllable TV cameras, the speedometer, and odometer (distance recording device). To control the vehicle properly, especially on curves, it should have an accelerometer;… …it will also need to know the mechanical state of the vehicle, so it will need an array of engine and electrical system sensors. It would have instruments that are not available to the average human driver: such as global positioning system (GPS) to give it accurate position information with respect to an electronic map; or infrared or SONAR sensors to detect distances to other cars and obstacles. In addition, it will need a microphone or keyboard for the passengers to tell it their destination.  control over the engine The actions available to the through the gas pedal automated taxi driver will be be the (accelerator) same ones available to a human driver: control over steering and braking. output to a screen or voice synthesizer to talk back to the In addition, it will need non- passengers conventional features such as: and perhaps some way to communicate with other vehicles.  In order to solve the problem at hand, there is the need to develop and implement a program that maps from percepts to actions. Consequently, this leads us to the discovery that different aspects of driving will require different types of agent programs. The five program types will now be discussed;  written as: if then if then  In the schematics you are about to see, we use… rectangles to denote the current internal state of the agent's decision process, and ovals to represent the background information used in the process. The problems associated with the simple reflex agent are: It is impossible to draw a complete look-up table of all possible percept and the corresponding actions. This is because the table will be too big to generate and store. It is not adaptive to changes in the environment. Updates have to be made in the table to accommodate changes. GOAL-BASED AGENTS Here, just knowing about the current state of the environment is not always enough to decide what to do. For example, at a road junction, the taxi can turn left, right, or go straight on. The right decision depends on where the taxi is trying to get to. In other words, as well as a current state description, the agent needs some sort of goal information, which describes situations that are desirable for example,. GOAL-BASED AGENTS The agent program can combine this with information about the results of possible actions (the same information as was used to update internal state in the reflex agent) in order to choose actions that achieve the goal. Sometimes this will be simple, when goal satisfaction results immediately from a single action; sometimes, it will be more tricky, when the agent has to consider long sequences of twists and turns to find a way to achieve the goal.  GOAL-BASED AGENTS Notice that decision-making of this kind is fundamentally different from the condition-action rules described earlier, in that it involves consideration of the future – both In the reflex agent designs, this information is not explicitly used, because the designer has pre-computed the correct action for various cases. A goal-based agent, in principle, could reason that if the car in front has its brake lights on, it will slow down. From the way the world usually evolves, the only action that will achieve the goal of not hitting other cars is to brake. GOAL-BASED AGENTS Clearly, the goal-based agent appears less efficient, but it is far more flexible. If it starts to rain, the agent can update its knowledge of how effectively its brakes will operate; this will automatically cause all of the relevant behaviours to be altered to suit the new conditions. For the reflex agent, on the other hand, we would have to rewrite a large number of condition-action rules. Certainly, the goal-based agent is also more flexible with respect to reaching different destinations. Simply by specifying a new destination, we can get the goal-based agent to come up with a new behavior. The reflex agent's rules for when to turn and when to go straight will only work for a single destination; they must all be replaced to go somewhere new. UTILITY-BASED AGENTS Goals alone are not really enough to generate high-quality behavior. For example, there are many action sequences that will get the taxi to its destination, thereby achieving the goal, but some are quicker, safer, more reliable, or cheaper than others. Goals just provide a crude distinction between "happy" and "unhappy" states, whereas a more general performance measure should allow a comparison of different world states (or sequences of states) according to exactly how happy they would make the agent if they could be achieved. Because "happy" does not sound very scientific, the customary terminology is to say that if one world state is preferred to another, then it has higher utility for the agent. UTILITY-BASED AGENTS Utility is therefore a function that maps a state onto a real number, which describes the. A complete specification of the utility function allows rational decisions in two kinds of cases where goals have trouble. First, when there are conflicting goals, only some of which can be achieved (for example, speed and safety), the utility function specifies the appropriate trade-off. Second, when there are several goals that the agent can aim for, none of which can be achieved with certainty, utility provides a way in which the likelihood of success can be weighed up against the importance of the goals. ] Agents and environments Agents include humans, robots, softbots, thermostats, etc. The agent function maps percept sequence to Actions f : P* → A An agent can perceive its own actions, but not always it effects The agent function will internally be represented by the agent program. The agent program runs on the physical architecture to produce f.