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+# AI - lesson 02
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+##### Nota: mancano i grafici nella parte finale (agent types)
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+#### Francesco Arrigoni
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+###### 9 October 2015
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+### Intelligent Agents
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+
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+|Agent
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+|---
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+|Environment
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+
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+An agent has a __perception__ of the environment and executes an __action__ on the former.
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+
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+- __P__: all the possible perceptions
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+- __A__: all the possible actions
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+
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+__P__ and __A__ are both finite sets.
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+
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+> Considering a camera with a resolution of 512x512 and 8bit depth, we have $256^{512x512}$ possible pictures.
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+It is a huge number but is finite.
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+
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+I can define an __agent function__:
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+$$f:P^* \rightarrow A$$
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+$|P^*|= \infty$, $P^+$ if we exclude $\varepsilon$
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+
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+<!--I just needed this comment for adjust syntax highlighting with ** -->
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+
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+An __agent program__ is a *computational model* of the agent function.
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+
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+---
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+#### Vacuum Cleaner example
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+
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+A|B
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+-|-
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+ __Perception__
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+
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+$P=\{[A,\text{dirty}],[A,\text{clean}],[B,\text{dirty}],[B,\text{clean}]\}$
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+
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+ If the robot had three sensors, the perception would be triple.
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+
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+ __Action__
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+
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+$A=\{\text{Right}, \text{Left}, \text{Suck}, \text{Moop}\}$
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+
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+ We can put the perceptions and actions in a __tabular form__
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+
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+$P^*$|$f$|$A$
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+---|---|---
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+$[A,\text{clean}]$||$\text{Right}$
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+$[A,\text{dirty}]$||$\text{Suck}$
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+$[B,\text{clean}]$||$\text{Left}$
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+$[B,\text{dirty}]$||$\text{Suck}$
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+$[A,\text{clean}]$|$[A,\text{clean}]$|$\text{Right}$
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+$[A,\text{clean}]$|$[A,\text{dirty}]$|$\text{Suck}$
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+...|...|...
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+
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+#### The environment is *dynamic*
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+
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+The environment is changing also while the agent does nothing.
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+It can happen that the environment is *nondeterministic*
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+
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+The __Environment__ can be
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+* __Dynamic__
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+* or __Stochastic__
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+
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+We now have to specify a __Performance Measure__.
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+
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+The best possible agent is called __rational agent__,
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+it selects actions in order to maximise the expected value of the performance function,
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+given what the agent knows so far.
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+
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+__Knowledge sources__:
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+* __Sequence of perceptions__
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+* __Initial knowledge__
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+
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+An agent is rational also if it doesn't know anything.
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+There is no need to know *everything* to be rational.
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+
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+An environment could be __fully__ or __partially__ observable.
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+It depends on the *sensors* of the agent.
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+e.g. a Google Car's environment is *partially observable*.
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+
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+__Environment__
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+- can be
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+ + __Discrete__ or __Continuous__
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+ + __Known__ or __Unknown__
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+ + __Fully__ or __Partially__ observable
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+ + __Single__ or __Multi__ agent
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+ + __Deterministic__ or __Stochastic__
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+ + __Episodic__ or __Sequential__
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+ + __Static__ or __Dynamic__
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+
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+We will focus on __single agent__, __deterministic__, __fully observable__, __static__ environments.
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+
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+Now we should move to the agent program.
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+
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+### Agent program
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+
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+#### simple reflex agents
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+for example ROOMBA
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+
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+__agent__
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+||||||
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+|---|---|---|---|---|
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+|||What the world look like|$\leftarrow$||
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+|||$\downarrow$||environment
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+|Action reuse|$\rightarrow$|What actually should i do|$\rightarrow$||
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+
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+This model performs well when the environment is *fully observable*.
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+
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+#### model-based reflex agents
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+
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+A baby that see a person that hides will think that he has disappered forever.
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+Model-based agents are able to *reconstruct* the state of a partially observable environment.
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+
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+#### goal-based agents
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+for example: chess
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+
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+This is the kind of agent that we will be studying.
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+
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+#### utility-based agents
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+
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+This agent will assign a real number to every possible actions according to the happiness of the agent in doing that action.
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+
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+All those types of agents are subject to learning.
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