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Artificial Intelligence (AI)

University of Science and Technology Faculty of Computer Science and Information Technology. Artificial Intelligence (AI). 4 th Year B.Sc : Information Technology Academic Year : 2017-2018 Instructor : Diaa Eldin Mustafa Ahmed. Intelligent Agents (IA)- (2/2).

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Artificial Intelligence (AI)

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  1. University of Science and Technology Faculty of Computer Science and Information Technology Artificial Intelligence (AI) 4th Year B.Sc : Information Technology Academic Year : 2017-2018 Instructor : Diaa Eldin Mustafa Ahmed Intelligent Agents (IA)- (2/2) AI - (2017-2018) -Diaa Eldein Mustafa - Lecture (2) _Intelligent Agent (2/2)

  2. Types of agent programs • Russell & Norvig (2003) group agents into six classes based on their degree of perceived intelligence and capability: • Table-Driven Agents • Simple reflex agents • Model-based reflex agents • Goal-based agents • Utility-based agents. • Learning agents • Complexity • Efficiency • Adaptability • Level of sophistication AI - (2017-2018) -Diaa Eldein Mustafa - Lecture (2) _Intelligent Agent (2/2)

  3. Table-Driven Agents • A trivial agent program: keeps track of the percept sequence and then uses it to index into a table of actions to decide what to do. • The designers must construct the table that contains the appropriate action for every possible percept sequence. AI - (2017-2018) -Diaa Eldein Mustafa - Lecture (2) _Intelligent Agent (2/2)

  4. Table-Driven Agents • Problems: • The table can become very large. • And it usually takes a very long time for the designer to specify it (or to learn it). • No autonomy. • Practically impossible. AI - (2017-2018) -Diaa Eldein Mustafa - Lecture (2) _Intelligent Agent (2/2)

  5. Simple Reflex Agents If (you see the car in front’s brake lights) thenapply the brakes Current State of Decision Process Table Driven Agent. Lookup table for entire history AI - (2017-2018) -Diaa Eldein Mustafa - Lecture (2) _Intelligent Agent (2/2)

  6. Simple Reflex Agents • Simple reflex agents act only on the basis of the current percept, ignoring the rest of the percept history. • Environment is fully observable , deterministic ,static , episodic , discreteandsingle agent. • The agent function is based on the condition-action rule: • ifconditionthenaction.e.g. : • If (you see the car in front’s brake lights) thenapply the brakes • Agent simply takes in a percept , determines which action could be applied, and does that action. AI - (2017-2018) -Diaa Eldein Mustafa - Lecture (2) _Intelligent Agent (2/2)

  7. Simple Reflex AgentsAdvantages / Disadvantages • Advantages: • Easy to implement. • Uses much less memory than the table-driven agent. • Useful when a quick automated response needed (i.e. reflex action is needed) . • Disadvantages: • Simple reflex agents can only react on fully observable environment. • Partially observable environments get simple reflex agents into trouble. • Possible condition-action rules too big to generateand to store (Chess has about states, for example) • Vacuum cleaner robot with defectivelocation sensor • leads to infinite loops. . AI - (2017-2018) -Diaa Eldein Mustafa - Lecture (2) _Intelligent Agent (2/2)

  8. Simple Reflex AgentsAdvantages / Disadvantages • Problems: • Table is too big to generate and store( e.g. Taxi Driver ) • Takes long time to build the table. • Looping :can’t make actions based on multi-level conditions . • Limited intelligence. • Having a model of the world does not always determine what to do(rationally). AI - (2017-2018) -Diaa Eldein Mustafa - Lecture (2) _Intelligent Agent (2/2)

  9. A Simple Reflex Agent in Nature percepts (size, motion) RULES: (1) If small moving object, then activate SNAP (2) If large moving object, then activate AVOID and inhibit SNAP ELSE (not moving) then NOOP Action: SNAP or AVOID or NOOP needed for completeness AI - (2017-2018) -Diaa Eldein Mustafa - Lecture (2) _Intelligent Agent (2/2)

  10. Simple Reflex AgentsVacuum-Cleaner World Agent • Environment:square A and B • Percepts: location and state of the • environment,e.g., [A,Dirty], [B,Clean] . • Actions:Left, Right, Suck, NoOp • Agent function:mapping from Percepts to Actions. • Agent Program: • procedure Reflex−Vacuum−Agent [location,status] returns an action • if status = Dirty then returnSuck • else if location = A then returnRight • else if location = B then returnLeft AI - (2017-2018) -Diaa Eldein Mustafa - Lecture (2) _Intelligent Agent (2/2)

  11. 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 AI - (2017-2018) -Diaa Eldein Mustafa - Lecture (2) _Intelligent Agent (2/2)

  12. Model-based Reflex Agents • The knowledge about “how the world works” is called a model of the world. • An agent that uses such a model is called a model- based agent. • Encode "internal state" of the world to remember the past as contained in earlier percepts (memory of the past). • 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" 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 . AI - (2017-2018) -Diaa Eldein Mustafa - Lecture (2) _Intelligent Agent (2/2)

  13. Model-based Reflex Agents • Upon getting a percept • Update the state (given the current state, the action you just did, and the observations). • Choose a rule to apply (whose conditions match the state). • Schedule the action associated with the chosen rule. • This agent can work even with partial information • It’s is unclear what to do without a clear goal. • Table lookup of condition-action pairs defining all possible condition-action rules • e.g : if (car – in – front – is –breaking) then (initiate breaking) • Little adaptive to changes in the environment , requires model information to be updated if changes occur. AI - (2017-2018) -Diaa Eldein Mustafa - Lecture (2) _Intelligent Agent (2/2)

  14. Model-based Reflex AgentsAlgorithm AI - (2017-2018) -Diaa Eldein Mustafa - Lecture (2) _Intelligent Agent (2/2)

  15. Goal-based Agents AI - (2017-2018) -Diaa Eldein Mustafa - Lecture (2) _Intelligent Agent (2/2)

  16. Goal-based Agents • With reflex agents, the goal is implicit in the condition-action rules. • With goal-based agents, we make the goal explicit. • This allows us to change the goal easily, and • Use search and planning in deciding the best action at any given time. • The heart of the goal based agent is the search function. • It returns a path from the current state to the goal--a plan. • The agent then returns the first action in the plan. • A simple enhancement would allow the agent to have multiple goals. • On the other hand, for the reflex-agent, we would have to • rewrite many condition-action rules. • The goal based agent's behavior can easily be changed . AI - (2017-2018) -Diaa Eldein Mustafa - Lecture (2) _Intelligent Agent (2/2)

  17. Goal-based Agents • Knowing about the current state of the environment • is not always enough to decide what to do (e.g. decision • at a road junction). • The agent needs some sort of goal information that • describes situations that are desirable . • The agent program can combine this with information about the results of possible actions in order to choose actions that achieve the goal. • Usually requires search and planning. • Although goal-based agents appears less efficient, it is more flexible because the knowledge that supports its decision is represented explicitly and can be modified. • The reflex agent's rules must be changed for a new situation. AI - (2017-2018) -Diaa Eldein Mustafa - Lecture (2) _Intelligent Agent (2/2)

  18. Utility-based Agents. • . AI - (2017-2018) -Diaa Eldein Mustafa - Lecture (2) _Intelligent Agent (2/2)

  19. Utility-based Agents • Goals alone are not really enough to generate high quality behavior in most environments – they just provide a binary distinction between happy and unhappy states. • A more general performance measure should allow a comparison of different world states according to exactly how happy they would make the agent if they could be achieved. • Happy – Utility (the quality of being useful). • A utility function maps a state onto a real number which describes the associated degree of happiness. AI - (2017-2018) -Diaa Eldein Mustafa - Lecture (2) _Intelligent Agent (2/2)

  20. Learning Agents Evaluates current World State Changes action rules “old agent” model world and decide with action to be taken Suggests explorations AI - (2017-2018) -Diaa Eldein Mustafa - Lecture (2) _Intelligent Agent (2/2)

  21. Learning Agents • Learning Agent Components • 1. Learning Element • responsible for making improvements (on what ever aspect is being learned...) • 2. Performance Element • responsible for selecting external actions. In previous parts, this was the entire agent! • 3. Critic • gives feedback on how agent is going and determines how performance element should be modified to do better in the future • 4. Problem Generator • suggests actions for new and informative experiences. AI - (2017-2018) -Diaa Eldein Mustafa - Lecture (2) _Intelligent Agent (2/2)

  22. Summary • Agents interact with environments through actuators and sensors. • The agent function describes what the agent does in all circumstances • The performancemeasure evaluates the environment sequence • A perfectly rational agent maximizes expected performance • Agent programs implement (some) agent functions • PEAS descriptions define task environments. • Environments are categorized along several dimensions:observable? deterministic? episodic? static? discrete? single-agent?. • Several basic agent architectures exist : reflex, reflex with state, goal-based, utility . AI - (2017-2018) -Diaa Eldein Mustafa - Lecture (2) _Intelligent Agent (2/2)

  23. Thank You End AI - (2017-2018) -Diaa Eldein Mustafa - Lecture (2) _Intelligent Agent (2/2)

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