CSC 450 - AI Intelligent Agents

Outline Agents and environments Rationality PEAS (Performance measure, Environment, Actuators, Sensors) Environment types Agent types

Agents An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators Human agent: eyes, ears, and other organs for sensors; Hands, legs, mouth, and other body parts for actuators Robotic agent: cameras and infrared range finders for sensors; various motors for actuators

Agents Percept Agent's perceptual inputs at any given instant Percept Sequence The complete history of everything the agent has ever perceived An agent's choice of action at any given instant can depend on the entire percept sequence observed to date

Agents and environments The agent function maps from percept histories to actions: [f: P*  A] The agent program runs on the physical architecture to produce f agent = architecture + program

Vacuum-cleaner world Percepts: location and contents, e.g., [A, Dirty] Actions: Left, Right, Suck, NoOp

A vacuum-cleaner agent Percept sequence Action [A, Clean] Right [A, Dirty] Suck [B, Clean] Left [B, Dirty] [A, Clean], [A, Clean] [A, Clean], [A, Dirty] [A, Clean], [A, Clean], [A, Clean] [A, Clean], [A, Clean], [A, Dirty] Partial tabulation of a simple agent function for the vacuum-cleaner world

Rational agents An agent should strive to "do the right thing", based on what it can perceive and the actions it can perform. The right action is the one that will cause the agent to be most successful Performance measure: An objective criterion for success of an agent's behavior E.g., performance measure of a vacuum-cleaner agent could be amount of dirt cleaned up, amount of time taken, amount of electricity consumed, amount of noise generated, etc.

Rational agents Rational Agent: For each possible percept sequence, a rational agent should select an action that is expected to maximize its performance measure, given the evidence provided by the percept sequence and whatever built-in knowledge the agent has.

Rational agents Agents can perform actions in order to modify future percepts so as to obtain useful information (information gathering, exploration) An agent is autonomous if its behavior is determined by its own experience (with ability to learn and adapt)

Agent Design PEAS: Performance measure, Environment, Actuators, Sensors( percept) Must first specify the setting for intelligent agent design Consider, e.g., the task of designing an automated taxi driver: Performance measure Environment Actuators Percepts

Agent Design Consider, e.g., the task of designing an automated taxi driver: Performance measure: Safe, fast, legal, comfortable trip, maximize profits Environment: Roads, other traffic, pedestrians, customers Actuators: Steering wheel, accelerator, brake, signal, horn percepts: Cameras, sonar, speedometer, GPS, odometer, engine sensors, keyboard

PEAS Agent: Medical diagnosis system Performance measure: Healthy patient, minimize costs, lawsuits Environment: Patient, hospital, staff Actuators: Screen display (questions, tests, diagnoses, treatments, referrals) Sensors: Keyboard (entry of symptoms, findings, patient's answers)

Agent Design Agent: Part-picking robot Performance measure: Percentage of parts in correct bins Environment: Conveyor belt with parts, bins Actuators: Jointed arm and hand Sensors: Camera, joint angle sensors

PEAS Agent: Interactive English tutor Performance measure: Maximize student's score on test Environment: Set of students Actuators: Screen display (exercises, suggestions, corrections) Sensors: Keyboard

Environment types Accessible (vs. inaccessible): An agent's sensors give it access to the complete state of the environment at each point in time. Deterministic (vs. stochastic): The next state of the environment is completely determined by the current state and the action executed by the agent. Episodic (vs. sequential): The agent's experience is divided into atomic "episodes" (each episode consists of the agent perceiving and then performing a single action), and the choice of action in each episode depends only on the episode itself.

Environment types Static (vs. dynamic): The environment is unchanged while an agent is deliberating. (The environment is semidynamic if the environment itself does not change with the passage of time but the agent's performance score does) Discrete (vs. continuous): A limited number of distinct, clearly defined percepts and actions. Single agent (vs. multiagent): An agent operating by itself in an environment.

Environment types Chess with Chess without Taxi driving a clock a clock Accessible Yes Yes No Deterministic Strategic Strategic No Episodic No No No Static Semi Yes No Discrete Yes Yes No The environment type largely determines the agent design The real world is (of course) partially accessible, stochastic, sequential, dynamic, continuous, multi-agent

INTELLIGENT AGENTS A Classification Table Driven agents simple reflex agents وكيل يعتمد على ردة فعل بسيطة model-based reflex agents وكيل يعتمد على نموذج وكيل مع ردة فعل goal-based agents وكيل ذو هدف معين utility-based agents وكيل قائم على التفضيل

Impractical 1. Table Driven Agent. table lookup for entire history current state of decision process Impractical table lookup for entire history

Fast but too simple 2. Simple reflex agents NO MEMORY Fails if environment is partially observable example: vacuum cleaner world

Simple reflex agents With perception Environment Agent see action

Example: Simple reflex agents Percept Action At A, A Dirty Vacuum At A, A Clean Move Left At B, B Dirty Vacuum At B, B Clean Move right

Simple reflex agents Simple but Limited functionality: Good when Environment is fully observable Condition -action rules have predicted all necessary actions.

3. Model-based reflex agents description of current world state Model the state of the world by: modeling how the world changes how it’s actions change the world This can work even with partial information It’s is unclear what to do without a clear goal

Model-based reflex agents With internal states Agent see action Predict state Environment

Model-based reflex agents Model-based reflex agents are made to deal with partial accessibility; they do this by keeping track of the part of the world it can see now. It does this by keeping an internal state that depends on what it has seen before so it holds information on the unobserved aspects of the current state.

Model-based reflex agents Action may depend on history or unperceived aspects of the world. Need to maintain internal world model. Example: Agent: robot vacuum cleaner Environment: dirty room, furniture. Model: map of room, which areas already cleaned. Sensor/model tradeoff.

4. Goal-based agents Goals provide reason to prefer one action over the other. We need to predict the future: we need to plan & search

Goal-based agents Agent Goals Decision see state Environment action Predict state Environment

Goal-based agents In life, in order to get things done we set goals for us to achieve, this pushes us to make the right decisions when we need to. A simple example would be the shopping list; our goal is to pick up every thing on that list. This makes it easier to decide if you need to choose between milk and orange juice because you can only afford one. As milk is a goal on our shopping list and the orange juice is not we chose the milk.

Goal-based agents Agents so far have fixed, implicit goals. We want agents with variable goals. Example: Agent: robot maid Environment: house & people. Goals: clean clothes, tidy room, table laid, etc

5. Utility-based agents Some solutions to goal states are better than others. Which one is best is given by a utility function. Which combination of goals is preferred?

Utility-based agents Just having goals isn’t good enough because often we may have several actions which all satisfy our goal so we need some way of working out the most efficient one. A utility function maps each state after each action to a real number representing how efficiently each action achieves the goal. This is useful when we either have many actions all solving the same goal or when we have many goals that can be satisfied and we need to choose an action to perform

Utility-based agents Agents so far have had a single goal. Agents may have to juggle conflicting goals. Need to optimize utility over a range of goals. Utility: measure of goodness (a real number). Combine with probability of success to get expected utility. Example: Agent: automatic car. Environment: roads, vehicles, signs, etc. Goals: stay safe, reach destination, be quick, obey law, save fuel, etc.

Learning agents How does an agent improve over time? By monitoring it’s performance and suggesting better modeling, new action rules, etc. Evaluates current world state changes action rules “old agent”= model world and decide on actions to be taken suggests explorations

Multi-Agent Systems Agent Agent ENVIRONMENT ENVIRONMENT Agent Agent

Multiagent Systems Features Interaction Communication languages Protocols Policies Co-ordination Co-operation Collaboration : Shared goals Negotiation

End of Lecture