Intelligent Agents 2 AI Slides (6e) c � Lin Zuoquan@PKU 1998-2020 2 1
2 Intelligence Agents 2.1 Agents 2.2 Agent programs 2.3 Rationality 2.4 Environments 2.5 Agent structures AI Slides (6e) c � Lin Zuoquan@PKU 1998-2020 2 2
Agents An agent is an entity that perceives and acts in an environment Agents include – animal agents – human agents – robotic agents (robots) – software agents (softbots) – – internet agents – – – crawler – – – webbot – – – email agent – – – search agent, etc. – – chatbots – – – Cortana/Siri/GAssistant/Waston/Alexa/FMessenger/ · · · Single agent or usually multi-agents (so-called distributed AI) AI Slides (6e) c � Lin Zuoquan@PKU 1998-2020 2 3
Agents and environments sensors percepts ? environment agent actions actuators An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators AI Slides (6e) c � Lin Zuoquan@PKU 1998-2020 2 4
Sensors and actuators A sensor measures some aspect of the environment in a form that can be used as input by an agent – vision, hearing, touch – radio, infrared, GPS, wireless signals – active sensing: send out a signal (such as radar or ultrasound) and sense the reflection of this signal off of the environment ⇒ IoT (Internet of Things) Perception provides agents with information about the world they inhabit by interpreting the response of sensors Actuators – hands, legs, vocal tract etc. – automated taxi: those available to a human driver e.g., accelerator, steering, braking and so on AI Slides (6e) c � Lin Zuoquan@PKU 1998-2020 2 5
Agent functions An agent is completely specified by the agent function : maps from percept histories to actions f : P ∗ → A For any given class of environments and tasks, we seek the agent (or class of agents) with the best performance Find a way to implement the rational agent function concisely AI Slides (6e) c � Lin Zuoquan@PKU 1998-2020 2 6
Agent programs The agent program runs on the physical architecture to produce the agent function agent = architecture + program program = algorithm + data The agent program takes a single percept as input, keeps internal state function Skeleton-Agent ( percept ) returns action persistent : memory , the agent’s memory of the world memory ← Update-Memory ( memory, percept ) action ← Choose-Best-Action ( memory ) memory ← Update-Memory ( memory, action ) return action The algorithm is described by the pseudocode AI Slides (6e) c � Lin Zuoquan@PKU 1998-2020 2 7
Another form of algorithm A procedure for the skeleton-agent that takes a single percept as input, keeps internal state Procedure of Skeleton-Agent Input : percept Output : action memory the agent’s memory of the world 1. memory ← Update-Memory ( memory, percept ) 2. action ← Choose-Best-Action ( memory ) 3. memory ← Update-Memory ( memory, action ) The algorithm is also described by the pseudocode AI Slides (6e) c � Lin Zuoquan@PKU 1998-2020 2 8
Algorithm Recall: an algorithm is an explicit effective set of instructions for a computing procedure – may be used to find the answers to any of a given class of questions – can be precisely defined by computational models, e.g., Turing machine, etc. Analysis of algorithms, independently of the particular implementation and input – time complexity: speed in seconds e.g., the worst T worst ( n ) or the average T avg ( n ) – space complexity: memory consumption in bytes Complexity analyzes problems rather than algorithms AI Slides (6e) c � Lin Zuoquan@PKU 1998-2020 2 9
Algorithm O -notation: asymptotic analysis T ( n ) is O ( f ( n )) if T ( n ) ≤ kf ( n ) for some k , for all n > 0 O ( n k ) for some k : polynomial time/space, called P or “easiness” Otherwise, exponential time/space, e.g., O (2 n ) , called NP , hardness – class of nondeterministic (Turing machine) polynomial problems guess a solution and then verify whether the guess is correct in polynomial time – NP -complete problems (the hardest subclass of NP ) – – co- NP (-complete) is the complement of NP (-complete) “yes” and“no” answers reversed Theorem: either all the NP -complete problems are in P or none of them is AI Slides (6e) c � Lin Zuoquan@PKU 1998-2020 2 10
Algorithm NP = P ?? if you could try all the guesses at once, or you were very lucky and always guess right the first time # P : counting problems corresponding to decision problems in NP decision problems have a yes-or-no answer, counting problems have an integer answer: how many solutions at least as hard as any NP problems PSPACE : require a polynomial amount of space, even on a non- deterministic machine it is believed that PSPACE -hard problems are worse than NP - complete problems although it could turn out that NP = PSPACE , just as P = NP AI Slides (6e) c � Lin Zuoquan@PKU 1998-2020 2 11
The pseudocode The pseudocode is a simple language to describe algorithms – similar to programming language like Java, C, Python or Lisp – informal to use mathematical formulas or ordinary English to describe parts • Persistent variables (global and local variables): a variable is given an initial value the first time a function is called and re- tains that value on all subsequent calls to the function. The agent programs use persistent variables for memory. Variables have low- ercase italic names. • Function as values : The value of a variable is allowed to be a function. Functions and procedures have capitalized names • Indentation is significant : the scope of a loop or conditional AI Slides (6e) c � Lin Zuoquan@PKU 1998-2020 2 12
The pseudocode • Assignment : ” x ← value ” means that the right-hand side eval- uate to the left-hand side variable – Destructuring assignment : ” x, y ← pair ” means that the right-hand side evaluate to a two-element tuple, and the first element is assigned to x and the second to y . Or ” for each x, y in pair do ” • if-then (-else) : ” if c then · · · else · · · ” means that if the con- dition c is hold then doing something; otherwise doing something else • for each : ” for each x in c do ” means that the loop is executed with the variable x bound to successive elements of the collection c . Or ” while c do ” or even ” loop · · · ” • Generators and yield : ” generator G ( x ) yields number” de- fines G as a generator function AI Slides (6e) c � Lin Zuoquan@PKU 1998-2020 2 13
The pseudocode • Lists : [ x, y, z ] , [ first | rest ] • Sets : { x, y, z } • Arrays start at 1 : as in usual mathematical notation, not 0 as in Java and C • /* the explanations can be given as remarks */ AI Slides (6e) c � Lin Zuoquan@PKU 1998-2020 2 14
The code The algorithms in the pseudocode can be implemented in Java, C/C++, Python, Lisp and Prolog etc. The (Lisp) code for each topic is divided into four directories: – agents : code defining agent types and programs – algorithms : code for the methods used by the agent programs – environments : code defining environment types, simulations – domains : problem types and instances for input to algorithms (Often run algorithms on domains rather than agents in environ- ments) AI Slides (6e) c � Lin Zuoquan@PKU 1998-2020 2 15
A coding (Lisp) (setq joe (make-agent :name ’joe :body (make-agent-body) :program (make-dumb-agent-program))) (defun make-dumb-agent-program () (let ((memory nil)) #’(lambda (percept) (push percept memory) ’no-op))) AI Slides (6e) c � Lin Zuoquan@PKU 1998-2020 2 16
Rationality A rational agent is one that does right thing – to achieve the best performance “goals” specifiable by performance measure defining a numerical value for any environment history Rational action: whichever action maximizes the expected value of the performance measure given the percept sequence to date Limited rationality : computational limitations make perfect ra- tionality unachievable ⇒ design best program for given machine resources Rational � = omniscient – percepts may not supply all relevant information Rational � = clairvoyant – action outcomes may not be as expected ⇒ rational � = successful AI Slides (6e) c � Lin Zuoquan@PKU 1998-2020 2 17
Vacuum-cleaner world Example: Robot cleaner, say, iRobot Roombat A B Percepts: location and contents, e.g., [ A, Dirty ] Actions: Left , Right , Suck , NoOp AI Slides (6e) c � Lin Zuoquan@PKU 1998-2020 2 18
A vacuum-cleaner agent Percept sequence Action [ A, Clean ] Right [ A, Dirty ] Suck [ B, Clean ] Left [ B, Dirty ] Suck [ A, Clean ] , [ A, Clean ] Right [ A, Clean ] , [ A, Dirty ] Suck . . . . . . function Reflex-Vacuum-Agent ( [ location , status ]) returns an action if status = Dirty then return Suck else if location = A then return Right else if location = B then return Left What is the right function? Can it be implemented in a program? AI Slides (6e) c � Lin Zuoquan@PKU 1998-2020 2 19
Vacuum-cleaner rational agent Fixed performance measure evaluates the environment sequence – one point per square cleaned up in time T ? – one point per clean square per time step, minus one per move? – penalize for > k dirty squares? AI Slides (6e) c � Lin Zuoquan@PKU 1998-2020 2 20
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