Block II, Unit III, Symbolic AI in the world

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Block II, Unit III, Symbolic AI in the world

• This unit has four main sections
• Planning
• Robots
• Learning adaptation and heuristics
• Uncertainty

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• Planning
• Planning might appear to be just another form of
  problem solving.
• In Symbolic AI, problem solving consists of
  setting a system to an initial state, defining a
  goal state and then defining all of the possible
  actions our system can take.
• The system will search through the space of
  possible states looking for a solution.

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• Planning
• To take a simple example, consider solving the
  problem of buying apples from a shop.
• The initial state is being at home with no
  apples, the goal state is being back at home with
  some apples.
• Between the two lies a state space that may be
  something like the one shown in following figure

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• Planning
• This is an oversimplified picture of the problem
• In reality, each level of the tree must have
  thousands, if not millions, of branches and the
  tree itself might have hundreds of levels.
• Exhaustive search of such a space is clearly
  infeasible, so heuristic techniques have to been
  brought in to speed up searches
• A good heuristic would tell the system that
  shopping is a good way of acquiring new items
  (including apples).

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• Planning
• The search could then be directed along the
  shopping branch.
• A further heuristic might then guide the search
  towards shops that sell fruit.
• But a more serious difficulty is that it forces
  the system to start either at the initial state
  or at the goal state and work towards the other
  the search program must examine each of the
  initial actions before moving on to the next.

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• Planning
• By comparison, planning relies on making direct
  connections between states and actions.
• Computers describe plans which are composed of
  states, goals and actions using a system of
  formal logic. Have some apples is an English
  language description of a goal
• The logical expression Have(apples) is its
  equivalent.
• Actions are described in the same manner

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• Planning
• Humans use their knowledge base to solve
  problems.
• Figure out a computer program attempting to solve
  this simple problem buying apples.
• With all the possible input and the encountered
  constraints, this will not be an easy job!!

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• Planning
• General actions Buy(x), which results having x

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• Sub-Planning
• The planning process allows for the problem to be
  broken into independent chunks known as sub-plans
• An example of the success and failure of
  sub-planning is illustrated in the following
  sections Blocks world.

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• Blocks world
• The real world is an incredibly complex and
  chaotic place.
• However, considering all of these fine details
  can obscure the detail of how planning (and other
  tasks) is done.
• One answer might be to eliminate all the messy
  details by constructing a very simple world in
  which the planner can operate
• The attention can be focused on the core problem,
  the construction of the plan.

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• Blocks world
• One such simplified world has played a leading
  part in the development of AI systems. It is
  usually known as Blocks World.
• Blocks World was used as an environment for early
  natural language understanding systems and robots
• Blocks World is closely linked with the problem
  of planning and with the early planning system,
  STRIPS.

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• Blocks world
• Blocks World is a tiny world comprising an
  (infinitely large) flat table on which sit a set
  of childrens building blocks.
• The blocks can be moved around and stacked on top
  of one another by a single robot hand.
• The hand can only hold one block at a time.
• Blocks world is most often simulated inside a
  computer, so all blocks are presumed to be
  perfectly regular, the movements of the arm
  infinitely precise.

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• Blocks world
• Planning in Blocks World means deciding the steps
  required to move blocks from an initial
  configuration (the start state) to another
  configuration (the goal state).
• On(B,C) OnTable(C) OnTable(A) HandEmpty

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• Blocks world
• The robot hand manipulates the world by picking
  up blocks and moving them around.
• A block x may only be picked up if both of the
  following are satisfied
• The robot hand is empty (HandEmpty).
• There is no block sitting on top of the selected
  block (Clear(x)).

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• Blocks world
• The hand can execute simple commands
• PickUp(A) picks up Block A, provided that the
  block is clear and the hand is empty whilst
• PutDown(A) places Block A on the table provided
  that the hand is holding the block.
• Stack(A,B) places Block A on top of Block B
  provided the hand is holding A and that the top
  face of B is clear
• UnStack(A,B) removes Block A from Block B
  provided that the hand is empty and that the top
  of A is clear.

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• Blocks world

To describe the state
On(x,y)
OnTable(x)
HandEmpty()
Clear(x)
Process/command
PickUp(x)
PutDown(x)
Stack(x,y)
UnStack(x,y)
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• Planning in the Blocks world
• Describe the initial state and the goal state of
  the following

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• Planning in the Blocks world divide the problem
• From the initial state we want to end up with
  Block A on the table, Block C on the table and
  Block B on top of Block A

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• Planning in the Blocks world
• The planner knows what actions it can perform,
  and the consequences of those actions.
• Actions are expressed as operators. Each operator
  has four parts its name, a set of preconditions,
  an add list and a delete list.
• The world changes with the execution of the
  operator, by specifying which facts are added to
  and deleted from the world state.

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• Planning in the Blocks world

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• Planning using means-end analysis STRIPS
• First, the goal conditions are added to the
  agenda.
• Planning then proceeds by popping the first
  condition from the agenda and, if its not
  already true, finding an operator that can
  achieve it.
• The operators action is then pushed on the
  agenda, as is each of the operators precondition
  terms.
• Achieving each of these preconditions requires
  its own sub-plan.
• The process continues until the only things left
  on the agenda are actions.
• If these are performed, in sequence, the goals
  will be achieved

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• STRIPS it starts with the three goals
  conditions being added to the agenda
• OnTable(A)
• On(B,A)
• OnTable(C)
• the topmost element, OnTable(A) is already true,
  so there is nothing to be done to achieve it, it
  is popped from the agenda and discarded
• The second term is not already true, so the
  system finds the Stack operator to achieve it.
  Stack(B,A) is pushed onto the agenda and the
  operators preconditions (Clear(A) and
  Holding(B)) are pushed on the agenda
• Clear(A)
• Holding(B)
• Stack(B,A)
• OnTable(C)

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• The process begins again.
• Clear(A) is already true, so that goal is
  discarded without action. Holding(B) will become
  true after an Unstack(B,C) operation, so that
  operator is pushed on the stack together with its
  preconditions, at this stage the agenda is
• Clear(B)
• On(B,C)
• UnStack(B,C)
• Stack(B,A)
• OnTable(C)

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• The top two goals in the stack are true, so are
  popped from the agenda
• The two operations (Unstack(B,C) and Stack(B,A))
  are performed in that order
• The final goal (OnTable(C)) is already true and
  so is removed.
• As the agenda is empty, all the goals have been
  achieved and the planning has succeeded.

Clear(B) On(B,C) UnStack(B,C) Stack(B,A) OnTable(C
)
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Example
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Goal state On(A,B) and On(B,C) and OnTable(C)
It is not always successful

• Sub-plans goals are achieved
• Plan is not achieved (sussman anomaly)
• The cause of the problem is the implementation
  order and the dependencies between sub-plans

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• Planning using means-end analysis STRIPS
  partial-order planning systems
• The technical term for when completing one
  sub-plan undoes the achievements of another is
  Clobbering
• Solution partial-order planning systems. The
  planner in this case commits itself to ensuring
  that the operations for each sub-plan occur in
  order, but they can be preceded, followed or
  interleaved with steps from other sub-plans
• Once all the actions for each sub-plan have been
  described, the planner attempts to combine the
  actions in such a way as to minimize clobbering.

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• Robots
• Purpose
• Categories/domains
• Medical
• Security
• Services
• Sub-marines work
• Manufacturing
• Mars missions
• Examples

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• Shakey (Stanford Robotics institute)
• 1966
• Lived in an indoor environment
• Can perform simple tasks, such as going from one
  room to another
• Nowadays, shakey is retired at the Computer
  History Museum in Mountain View, California, USA

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• Shakey the robot

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• The Soviet Union moon probe Lunokhod
• On November 1970, Lunokhod entered the moon orbit
• The first remotely operated vehicle to explore
  another world
• Its length was 2.3 meters, its weight is around
  750Kg
• The rover would run during the lunar day,
  stopping occasionally to recharge its batteries
  via the solar panels.
• At night the rover hibernated until the next
  sunrise, heated by the radioactive source.
• Controllers finished the last communications
  session with Lunokhod 1 at 1305 UT on September
  14, 1971
• Lunokhod has been located by a research team from
  the University of California at San Diego in 2010

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• The Soviet Union moon rover Lunokhod

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• Spirit and Opportunity Mars exploration rovers
• Launched from earth in 2003
• Landed on Mars early 2004
• Opportunity robot standing 1.5 m, high, 2.3 m
  wide and 1.6 m long and weighing 180 kg
• Both rovers still alive, transferring images and
  Mars soil test on daily basis, in addition to
  other scientific results about Mars

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• Opportunity Mars exploration rover

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• Beagle 2 Mars exploration rovers (laboratory)
• Beagle 2 was an unsuccessful British landing
  spacecraft that formed part of the European Space
  Agency's 2003 Mars Express mission.
• It is not known for certain whether the lander
  reached the Martian surface
• All contact with it was lost upon its separation
  from the Mars Express six days before its
  scheduled entry into the atmosphere.
• It may have missed Mars altogether, skipped off
  the atmosphere and entered an orbit around the
  sun, or burned up during its descent.
• If it reached the surface, it may have hit too
  hard or else failed to contact Earth due to a
  fault.
• It was a promising mission, Beagle 2 held
  advanced laboratory

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• Learning, Adaptation and Heuristics
• One characteristic that we would surely associate
  with an intelligent individual, natural or
  artificial, is the ability to learn from its
  environment, whether this means widening the
  range of tasks it can perform or performing the
  same tasks better.
• If we really want to understand the nature of
  intelligence, we have to understand learning.
• Another reason for investigating learning is to
  make the development of intelligent systems
  easier
• Rather than equipping a system with all the
  knowledge it needs, we can develop a system that
  begins with adequate behavior, but learns to
  become more competent.
• The ability to learn is also the ability to adapt
  to changing circumstances, a vital feature of any
  system.

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• Learning, Adaptation and Heuristics
• In Symbolic AI systems, behavior is governed by
  the processes defined for that system.
• If a system is to learn, it must alter these, by
  either modifying existing processes or adding new
  ones.
• Many existing learning systems have the task of
  classification the system is presented with a
  set of examples and learns to classify these into
  different categories.
• The learning can be either supervised (where the
  correct classifications are known to the learner)
  or unsupervised (where the learner has to work
  out the classifications for itself).

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• Learning, Adaptation and Heuristics
• Other approaches to automated learning include
• speed-up learning In speed-up learning a system
  remembers situations it has been in before and
  the actions it took then. When it encounters a
  similar situation later, it decides on an action
  by remembering what it did last time, rather than
  determining it from first principles all over
  again
• inductive programming A learning system is
  presented with the inputs and desired outputs of
  a program or procedure. The system has to derive
  the program that satisfies these constraints.

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• Decision trees
• A decision tree is a way of classifying objects
  or situations.
• Each leaf node of the tree represents a class the
  object could belong to
• Each internal node represents a test to get the
  value of an attribute of the object.
• As each attribute is tested, we move down the
  tree until we reach a correct classification.
• So a decision tree is a way of representing an
  order in which to ask questions about an object
  (or directly observe its attributes) in order to
  place it in the right class.

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• Decision trees, an example

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• Training data

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• Training data and learning
• A decision tree is a way of classifying objects
  or situations.
• We identify the most discriminating attribute for
  the decision and to split the data on the value
  of that attribute.
• For instance, in the data shown in Table 3.4, the
  most discriminating attribute seems to be
  Schedule? if the student is behind schedule,
  the student will always study if she is on
  schedule, she studies more often than not
  otherwise she will often watch TV.
• The next most important attribute appears to be
  Good TV? if there is nothing good on the TV
  she will nearly always study M366.
• Building the tree up level by level leads to the
  following partial tree

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• Decision tree learned from the data table

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• Decision tree learned from the data table

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• Uncertainty
• AI systems are expected to move outside the
  laboratory so they must face a world that is
  complex and, above all, uncertain
• They will have to cope with that uncertainty.
• As we all know, most human judgments are
  provisional. For instance
• when a weather forecaster informs us that it is
  going to rain tomorrow, we know that she is not
  really expressing definite knowledge she is only
  offering a probability.
• AI community has developed strategies for
  reasoning about situations where precise
  information is either unavailable or unnecessary.

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• Uncertainty
• The issue of uncertainty first came to prominence
  in diagnostic expert systems such as MYCIN, a
  program for diagnosing bacterial blood
  infections.
• Such systems have to account for imprecision in
  the results of tests and non-certain reasoning
  steps, for example
• IF the stain of the organism is gram-positive
• AND the morphology of the organism is coccus
• AND the growth conformation of the organism is
  clump
• THEN (0.7) the identity of the organism is
  staphylococcus
• Here, the 0.7 is the certainty factor of this
  conclusion given the antecedents.
• The certainty factors of each deduction enabled
  MYCIN to track how reliable it believed each
  conclusion to be, and to report a final, combined
  certainty for the reliability of its diagnosis
  back to the user.

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• Uncertainty Bayesian probability statistics
• An AI approach that is widely used, is based on
  mathematical probability theory and Bayesian
  probability statistics.
• In the Bayesian view of probability, the
  probability of a propositions being true
  reflects the strength of our belief in that
  proposition, generally in the light of some
  supporting information.
• The prior probability of a proposition h (such as
  the battery is flat) is written P ( h ) .
• If we have some evidence e that can influence the
  probability of h (i.e. the lights are dim), we
  can deduce the posterior or conditional
  probability of the proposition h given e , which
  we denote as P ( h e ) .

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• Uncertainty Bayesian probability statistics
• P(e h) is the probability of e being true if h
  is true
• Example page146
• The results is the probability of having fire
  given that the alarm sounds is 0.0094

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• Fuzzy logic
• fuzzy logic deals with the situation where we
  know all about an entity but it belongs to more
  than one category.
• Consider this question am I (are you) very tall,
  tall, medium or short? Which category do I (do
  you) belong to?
• Theres no cut-and-dried answer to this question.
  Im fairly tall taller than most of my
  colleagues but a dwarf compared to the average
  American basketball player.
• Im much taller than, say, the landlady of my
  house.
• Illustration are presented on the next figure

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• Fuzzy logic
• fuzzy logic the boundaries are fuzzy, this means
  that a person might be tall in some contexts but
  short in others .