Plans for Today

1
Plans for Today

• Chapter 2 Intelligent Agents (until break)
• Lisp Some questions that came up in lab
• Resume intelligent agents after Lisp issues

2
Intelligent Agents

• Agent anything that can be viewed as
• perceiving its environment through sensors
• acting upon its environment through actuators
• Examples
• Human
• Web search agent
• Chess player
• What are sensors and effectors for each of these?

3
Rational Agents

• Conceptually one that does the right thing
• Criteria Performance measure
• Performance measures for
• Web search engine?
• Tic-tac-toe player? Chess player?
• When performance is measured plays a role
• short vs. long term

4
Rational Agents

• Omniscient agent
• Knows actual outcome of its actions
• What info would chess player need to be
  omniscient?
• Omniscience is (generally) impossible
• Rational agent should do right thing based on
  knowledge it has

5
Rational Agents

• What is rational depends on four things
• Performance measure
• Percept sequence everything agent has seen so
  far
• Knowledge agent has about environment
• Actions agent is capable of performing
• Rational Agent definition
• Does whatever action is expected to maximize its
  performance measure, based on percept sequence
  and built-in knowledge

6
Autonomy

• Independence
• A system is autonomous if its behavior is
  determined by its percepts
• An alarm that goes off at a prespecified time is
  not autonomous
• An alarm that goes off when smoke is sensed is
  autonomous
• A system without autonomy lacks flexibility

7
The Task Environment

• An agents rationality depends on
• Performance Measure
• Environment
• Actuators
• Sensors
• What are each of these for
• Chess Player?
• Web Search Tool?
• Matchmaker?
• Musical performer?

8
Environments Fully Observable vs. Partially
Observable

• Fully observable agents sensors detect all
  aspects of environment relevant to deciding
  action
• Examples?
• Which is more desirable?

9
Environments Determinstic vs. Stochastic

• Deterministic next state of environment is
  completely determined by current state and agent
  actions
• Stochastic uncertainty as to next state
• If environment is partially observable but
  deterministic, may appear stochastic
• If environment is determinstic except for actions
  of other agents, called strategic
• Agents point of view is the important one
• Examples?
• Which is more desirable?

10
Environments Episodic vs. Sequential

• Episodic Experience is divided into episodes
  of agent perceiving then acting. Action taken in
  one episode does not affect next one at all.
• Sequential typically means need to do lookahead
• Examples?
• Which is more desirable?

11
Environments Static vs. Dynamic

• Dynamic Environment can change while agent is
  thinking
• Static Environment does not change while agent
  thinks
• Semidynamic Environment does not change with
  time, but performance score does
• Examples?
• Which is more desirable?

12
Environments Discrete vs. Continuous

• Discrete Percepts and actions are distinct,
  clearly defined, and often limited in number
• Examples?
• Which is more desirable?

13
Environments Single agent vs. multiagent

• What is distinction between environment and
  another agent?
• for something to be another agent, maximize a
  performance measure depending on your behavior
• Examples?

14
Structure of Intelligent Agents

• What does an agent program look like?
• Some extra Lisp Persistence of state (static
  variables)
• Allows a function to keep track of a variable
  over repeated calls.
• Put functions inside a let block
• (let ((sum 0)) (defun myfun (x) (setf sum
  ( sum x))) (defun report () sum))

15
Generic Lisp Code for an Agent

• (let ((memory nil)) (defun skeleton-agent
  (percept) (setf memory (update-memory
  memory percept)) (setf action
  (choose-best-action memory)) (setf memory
  (update-memory memory action)) action
  return action ))

16
Table Lookup Agent

• In theory, can build a table mapping percept
  sequence to action
• Inputs percept
• Outputs action
• Static Variable percepts, table

17
Lookup Table Agent

• (let ((percepts nil) (table ????) (defun
  table-lookup-agent (percept) (setf percepts
  (append (list percept) percepts)) (lookup
  percepts table)) ))

18
Specific Agent ExamplePathfinder (Mars Explorer)

• Performance Measure
• Environment
• Actuators
• Sensors
• Would table-driven work?

19
Four kinds of better agent programs

• Simple reflex agents
• Model-based reflex agents
• Goal-based agents
• Utility-based agents

20
Simple reflex agents

• Specific response to percepts, i.e.
  condition-action rule
• if new-boulder-in-sight then move-towards-new-bo
  ulder
• Advantages
• Disadvantages

21
Model-based reflex agents

• Maintain an internal state which is adjusted by
  each percept
• Internal state looking for a new boulder, or
  rolling towards one
• Affects how Pathfinder will react when seeing a
  new boulder
• Can be used to handle partial observability by
  use of a model about the world
• Rule for action depends on both state and percept
• Different from reflex, which only depends on
  percept

22
Goal-Based Agents

• Agent continues to receive percepts and maintain
  state
• Agent also has a goal
• Makes decisions based on achieving goal
• Example
• Pathfinder goal reach a boulder
• If pathfinder trips or gets stuck, can make
  decisions to reach goal

23
Utility-Based Agents

• Goals are not enough need to know value of goal
• Is this a minor accomplishment, or a major one?
• Affects decision making will take greater risks
  for more major goals
• Utility numerical measurement of importance of a
  goal
• A utility-based agent will attempt to make the
  appropriate tradeoff

24
Lisp Questions
25
Why the dot in cons?Two Explanations

• High level cons expects a list in the second
  position
• Lower level
• Cons takes a cons cell from the free storage list
• Puts first argument in first position
• Puts second argument in rest position
• Separates by a dot, unless rest position is a
  pointer (indicates continuing list)

26
How does append work?

• Makes copy of first list
• Takes last pointer and points to second list
• Picture

27
How to debug?

• Can trace function calls with (trace function)
  and (untrace function)
• Demonstration with mystery function from lab
• At a Breakgt prompt, can see call stack with
• backtrace
• Can go through code step by step
• (step (mystery 2 3))
• Use step and next to go through each function as
  you go along
• Use (print var)

28
Random bits

• Why the p in (zerop x)?
• p predicate
• NOT true that p positive

29
Scoping and binding

• let declares a scope where variable bindings are
  insulated from outside
• usual notions of local and global variables apply
• if you want to change a global variable from
  within a function