Block II, Unit III, Symbolic AI in the world

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 children’s 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

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To describe the state

On(x,y)

OnTable(x)

HandEmpty()

Clear(x)

Process/command

PickUp(x)

PutDown(x)

Stack(x,y)

UnStack(x,y)


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 it’s not already true, finding an operator that can achieve it.

The operator’s action is then pushed on the agenda, as is each of the operator’s 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 operator’s 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.

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Clear(B)

On(B,C)

UnStack(B,C)

Stack(B,A)

OnTable(C)


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Example


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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It is not always successful

Goal state: On(A,B) and On(B,C) and OnTable(C)


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 13:05 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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Block II, Unit III, Symbolic AI in the worldLearning, 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 HeuristicsOther 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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Block II, Unit III, Symbolic AI in the worldUncertainty:

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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Block II, Unit III, Symbolic AI in the worldUncertainty: 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 proposition’s 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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)(

)()|()|(

eP

hPhePehP


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? There’s no cut-and-dried answer to this question. I’m fairly tall –

taller than most of my colleagues – but a dwarf compared to the average American basketball player.

I’m 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 .

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Block II, Unit III, Symbolic AI in the world