Strong Method Problem Solving

1
Strong Method Problem Solving
7
7.0 Introduction 7.1 Overview of Expert System
Technology 7.2 Rule-Based Expert Systems 7.3 Mode
l-Based, Case Based, and Hybrid Systems
7.4 Planning 7.5 Epilogue and References 7.6 Ex
ercises
2
Chapter Objectives

• Learn about knowledge-intensive AI applications
• Learn about the issues in building Expert
  Systems knowledge engineering, inference,
  providing explanations
• Learn about the issues in building Planning
  Systems writing operators, plan generation,
  monitoring execution
• The agent model Can perform expert quality
  problem solving can generate and monitor plans

3
Expert systems (ESs) - motivations

• Experts usually have a lot of knowledge, why not
  build a system that incorporates a lot of
  knowledge in a specific area.
• Will attempt to solve a problem that is
• non-trivial
• complex
• poorly understood
• The resulting system will be
• fast
• reliable
• cheap
• transportable
• usable in remote sites

4
What is in an expert system?

• lots of knowledge
• a production system architecture
• inference techniques
• advanced features for the user
• should make their job easier
• explanations

5
Guidelines to determine whether a problem is
appropriate for an ES solution

• The need for the solution justifies the cost and
  effort of building an expert system.
• Human expertise is not available in all
  situations where it is needed.
• The problem may be solved using symbolic
  reasoning.
• The problem domain is well structured and does
  not require commonsense reasoning.
• The problem may not be solved using traditional
  computing methods.
• Cooperative and articulate experts exist.
• The problem is of proper size and scope.

6
Architecture of a typical expert system
7
The role of mental or conceptual models in
problem solving.
8
Exploratory development cycle
9
A small ES for diagnosing automotive problems
If the engine is getting gas, and the engine will
turn over, then the problem is spark
plugs If the engine does not turn over, and the
lights do not come on then the problem is
battery or cables
If the engine does not turn over, and the lights
do come on, then the problem is the starter
motor If there is gas in the fuel tank,
and there is gas in the carburetor then the
engine is getting gas
1
3
2
4
10
The and/or graphsearched in the car diagnosis
example
11
The production system at the start of a
consultation
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The production system after Rule 1 has fired
13
The system after Rule 4 has fired. Note the
stack-based approach to goal reduction
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Explanation Answering why questions

• ES - Gas in fuel tank?U- yesES- Gas in
  carburetor?U- yesES- Engine will turn
  over?U- why
• ES- It has been established that 1. The engine
  is getting gas, therefore if 2. The engine will
  turn over, then the problem is spark plugs.
• The user is asking why is this information
  important?

15
Explanation Answering how questions

• U- how the engine is getting gas
• ES- This follows from rule 4 if gas in fuel
  tank, and there is gas in the carburetor then t
  he engine is getting gas gas in fuel tank was
  given by the user gas in the carburetor was
  given by the user
• The user is asking How did the system come up
  with this conclusion?

16
The production system at the start of a
consultation for data-driven reasoning
17
The production system after evaluating the first
premise of Rule 2, which then fails
18
After considering Rule 4, beginning its second
pass through the rules
19
The search graph as described by the contents of
WM data-driven BFS
20
ES examples - DENDRAL(Russell Norvig, 2003)

• DENDRAL is the earliest ES(project 1965- 1980)
• Developed at Stanford by Ed Feigenbaum, Bruce
  Buchanan, Joshua Lederberg, G.L. Sutherland,
  Carl Djerassi.
• Problem solved inferring molecular structure
  from the information provided by a mass
  spectrometer. This is an important problem
  because the chemical and physical properties of
  compounds are determined not just by their
  constituent atoms, but by the arrangement of
  these atoms as well.

21
ES examples - DENDRAL(Russell Norvig, 2003)

• Inputs elementary formula of the molecule (e.g.,
  C6H13NO2), and the mass spectrum giving the
  masses of the various fragments of the molecule
  generated when it is bombarded by an electron
  beam (e.g., the mass spectrum might contain a
  peak at m15, corresponding to the mass of a
  methyl (CH3) fragment.

22
ES examples - DENDRAL (contd)

• Naïve version DENDRAL stands for DENDritic
  Algoritm a procedure to exhaustively and
  nonredundantly enumerate all the topologically
  distinct arrangements of any given set of atoms.
  Generate all the possible structures consistent
  with the formula, predict what mass spectrum
  would be observed for each, compare this with the
  actual spectrum.This is intractable for large
  molecules!
• Improved version look for well-known patterns of
  peaks in the spectrum that suggested common
  substructures in the molecule. This reduces the
  number of possible candidates enormously.

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ES examples - DENDRAL (contd)

• A rule to recognize a ketone (C0) subgroup
  (weighs 28)
• if there are two peaks at x1 and x2 such that(a)
  x1 x2 M 28 (M is the mass of the whole
  molecule)(b) x1 - 28 is a high peak(c) x2 - 28
  is a high peak(d) at least one of x1 and x2 is
  highthen there is a ketone subgroup

Cyclopropyl-methyl-ketone
Dicyclopropyl-methyl-ketone
24
ES examples - MYCIN

• MYCIN is another well known ES.
• Developed at Stanford by Ed Feigenbaum, Bruce
  Buchanan, Dr. Edward Shortliffe.
• Problem solved diagnose blood infections. This
  is an important problem because physicians
  usually must begin antibiotic treatment without
  knowing what the organism is (laboratory cultures
  take time). They have two choices (1)
  prescribe a broad spectrum drug (2) prescribe a
  disease-specific drug (better)
• .

25
ES examples - MYCIN (contd)

• Differences from DENDRAL
• No general theoretical model existed from which
  MYCIN rules could be deduced. They had to be
  acquired from extensive interviewing of experts,
  who in turn acquired them from textbooks, other
  experts, and direct experience of cases.
• The rules reflected uncertainty associated with
  medical knowledge certainty factors (not a sound
  theory)

26
ES examples - MYCIN (contd)

• About 450 rules. One example is
• If the site of the culture is blood the gram
  of the organism is neg the morphology of the
  organism is rod the burn of the patient is
  seriousthen there is weakly suggestive
  evidence (0.4) that the identity of the
  organism is pseudomonas.

27
ES examples - MYCIN (contd)

• If the infection which requires therapy is
  meningitis only circumstantial evidence is
  available for this case the type of the
  infection is bacterial the patient is receiving
  corticosteroids then there is evidence that
  the organisms which might be causing the
  infection are e.coli(0.4), klebsiella-
  pneumonia(0.2), or pseudomonas-aeruginosa(0.1).

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ES examples - MYCIN (contd)

• Starting rule If there is an organism requiring
  therapy, then, compute the possible therapies and
  pick the best one.
• It first tries to see if the disease is known.
  Otherwise, tries to find it out.

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ES examples - MYCIN (contd)

• Can ask questions during the process
• gt What is the patients name? John Doe.gt Male
  or female? Male.gt Age? He is 55.gt Have you
  obtained positive cultures indicating general
  type? Yes.gt What type of infection is
  it? Primary bacteremia.

30
ES examples - MYCIN (contd)

• gt Lets call the first significant
  organism from this culture U1. Do you know
  the identity of U1? No.gt Is U1 a rod or a
  coccus or something else? Rod.gt What is the
  gram stain of U1? Gram-negative.
• In the last two questions, it is trying to ask
  the most general question possible, so that
  repeated questions of the same type do not annoy
  the user. The format of the KB should make the
  questions reasonable.

31
ES examples - MYCIN (contd)

• Studies about its performance showed its
  recommendations were as well as some experts, and
  considerably better than junior doctors.
• Could calculate drug dosages very precisely.
• Dealt well with drug interactions.
• Had good explanation features and rule
  acquisition systems.
• Was narrow in scope (not a large set of
  diseases). Another expert system, INTERNIST,
  knows about internal medicine.
• Issues in doctors egos, legal aspects.

32
Asking questions to the user

• Which questions should be asked and in what
  order?
• Try to ask questions to make facilitate a more
  comfortable dialogue. For instance, ask related
  questions rather than bouncing between unrelated
  topics (e.g., zipcode as part of an address or to
  relate the evidence to the area the patient
  lives).

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ES examples - R1 (or XCON)

• The first commercial expert system (1982).
• Developed at Digital Equipment Corporation (DEC).
• Problem solved Configure orders for new computer
  systems. Each customer order was generally a
  variety of computer products not guaranteed to be
  compatible with one another (conversion cards,
  cabling, support software)
• By 1986, it was saving the company 40 million a
  year. Previously, each customer shipment had to
  be tested for compatibility as an assembly before
  being shipper. By 1988, DECs AI group had 40
  expert systems deployed.

34
ES examples - R1 (or XCON) (contd)

• Rules to match computers and their peripherals
• If the Stockman 800 printer and DPK202 computer
  have been selected, add a printer conversion
  card, because they are not compatible.
• Being able to change the rule base easily was an
  important issue because the products were always
  changing.
• Over 99 of the configurations were reported to
  be accurate. Errors were due to lack of product
  information on recent products (easily
  correctible.) Like MYCIN, performs as well as or
  better than most experts.
• 6,000 - 10,000 rules.

35
Expert Systems then and now

• The AI industry boomed from a few million
  dollars in 1980 to billions of dollars in 1988.
• Nearly every major U.S. corporation had its own
  AI group and was either using or investigating
  expert systems.
• For instance, Du Pont had 100 ESs in use and 500
  in development, saving an estimated 10 million
  per year.
• AAAI had 15,000 members during the expert
  systems craze.
• Soon a period called the AI Winter
  cameBIRRR...

36
Expert Systems then and now (contd)

• The AI industry has shifted focus and stabilized
  (AAAI members 5500- 7000)
• Expert systems continue to save companies money
• IBMs San Jose facility has an ES that diagnoses
  problems on disk drives
• Pac Bells diagnoses computer network problems
• Boeings tells workers how to assemble electrical
  connectors
• American Express Cos helps in card application
  approvals
• Met Lifes processes mortgage applications
• Expert Sytem Shells abstract away the details
  to produce an inference engine that might be
  useful for other tasks. Many are available.

37
Heuristics and control in expert systems

• organization of a rules premises
• rule order
• costs of different tests
• which rules to select
• refraction
• recency
• specificity
• restrict potentially usable rules

38
Model-based reasoning

• Attempt to describe the inner details of the
  system.
• This way, the expert system (or any other
  knowledge-intensive program) can revert to first
  principles, and can still make inferences if
  rules summarizing the situation are not present.
• Include a description of
• each component of the device,
• devices internal structure,
• observations of the devices actual performance

39
The behavioral description of an adder (Davis and
Hamscher,1988)
Behaviour at the terminals of the device e.g., C
is AB.
40
Taking advantage of direction of information flow
(Davis and Hamscher, 1988)
Either ADD-1 is bad, or the inputs are
incorrect (MULT-1 or MULT-2 is bad)
41
Fault diagnosis procedure

• Generate hypotheses identify the faulty
  component(s) , e.g., ADD-1 is not faulty
• Test hypotheses Can they explain the observed
  behaviour?
• Discriminate between hypotheses What additional
  information is necessary to resolve conflicts?

42
A schematic of the simplified Livingstone
propulsion system (Williams and Nayak ,1996)
43
A model-based configuration management system
(Williams and Nayak, 1996)
44
Case-based reasoning (CBR)

• Allows reference to past cases to solve new
  situations.
• Ubiquitous practice medicine, law, programming,
  car repairs,

45
Common steps performed by a case-based reasoner

• Retrieve appropriate cases from memory
• Modify a retrieved case so that it will apply to
  the current situation
• Apply the transformed case
• Save the solution, with a record of success or
  failure, for future use

46
Preference heuristics to help organize the
storage and retrieval cases (Kolodner, 1993)

• Goal directed preference Retrieve cases that
  have the same goal as the current situation
• Salient-feature preference Prefer cases that
  match the most important features or those
  matching the largest number of important features
• Specify preference Look for as exact as
  possible matches of features before considering
  more general matches
• Recency preference Prefer cases used most
  recently
• Ease of adaptation preference Use first cases
  most easily adapted to the currrent situation

47
Transformational analogy (Carbonell, 1983)
48
Advantages of a rule-based approach

• Ability to directly use experiential knowledge
  acquired from human experts
• Mapping of rules to state space search
• Separation of knowledge from control
• Possibility of good performance in limited
  domains
• Good explanation facilities

49
Disadvantages of a rule-based approach

• highly heuristic nature of rules not capturing
  the functional (or model-based) knowledge of the
  domain
• brittle nature of heuristic rules
• rapid degradation of heuristic rules
• descriptive (rather than theoretical) nature of
  explanation rules
• highly task dependent knowledge

50
Advantages of model-based reasoning

• Ability to use functional/structure of the
  domain
• Robustness due to ability to resort to first
  principles
• Transferable knowledge
• Aibility to provide causal explanations

51
Advantages of model-based reasoning

• Lack of experiental (descriptive) knowledge of
  the domain
• Requirement for an explicit domain model
• High complexity
• Unability to deal with exceptional situations

52
Advantages of case-based reasoning

• Ability to encode historical knowledge directly
• Achieving speed-up in reasoning using shortcuts
• Avoiding past errors and exploiting past
  successes
• No (strong) requirement for an extensive
  analysis of domain knowledge
• Added problems solving power via appropriate
  indexing strategies

53
Disadvantages of case-based reasoning

• No deeper knowledge of the domain
• Large storage requirements
• Requirement for good indexing and matching
  criteria

54
How about combining those approaches?

• Complex!! But nevertheless useful.
• rule-based case-based can
• first check among previous cases, then engage in
  rule-based reasoning
• provide a record of examples and exceptions
• provide a record of searches done

55
How about combining those approaches?

• rule-based model-based can
• enhance explanations with functional knowledge
• improve robustness when rules fail
• add heuristic search to model-based search
• model-based case-based can
• give more mature explanations to the situations
  recorded in cases
• first check against stored cases before
  proceeding with model-based reasoning
• provide a record of examples and exceptions
• record results of model-based inference
• Opportunities are endless!

56
What is planning?

• It is a system whose task is to find a sequence
  of actions to accomplish a specific task.
• The main components of a planning problem are
• a description of the starting situation (the
  initial state),
• a description of the desired situation (the goal
  state),
• the actions available to the executing agent
  (operator library, aka domain theory).
• Formally, a (classical) planning problem is a
  triple ltI, G, Dgt, where I is the initial state,
  G is the goal state, and D is the domain theory.

planner
planning problem
plan
57
Characteristics of classical planners

• They need a mechanism to reason about actions
  and the changes they inflict on the world
• Important assumptions
• the agent is the only source of change in the
  world, otherwise the environment is static
• all the actions are deterministic
• the agent is omniscient knows everything it
  needs to know about start state and effects of
  actions
• the goals are categorical, the plan is considered
  successful iff all the goals are achieved

58
The blocks world
59
Represent this world using predicates

• ontable(a)ontable(c)ontable(d)on(b,a)on(e,d)c
  lear(b)clear(c)clear(e)gripping()

60
Declarative (or procedural) rules

• If a block is clear, then there are no blocks on
  top of it (declarative)
• OR
• To make sure that a block is clear, make sure to
  remove all the blocks on top of it (procedural)
• 1. (?X) ( clear(X) ? ? (?Y) ( on(Y, X) ))
• 2. (?Y)(?X) ? on(Y, X) ? ontable(Y)
• 3. (?Y) gripping() ? ? gripping(Y)

61
Rules for operation on the states

• 4. (?X) pickup(X) ? (gripping(X) ?
  (gripping() ? clear(X) ? ontable(X)))
• 5. (?X) putdown(X) ? (gripping() ?
  ontable(X) ? clear(X) ? (gripping(X)))
• 6. (?X) stack(X,Y) ? ((on (X,Y) ?
  gripping() ? clear(X)) ? (clear(Y) ?
  gripping(X)) )
• 7. (?X) unstack(X,Y) ? ((clear(Y) ?
  gripping(X) ) ? (on(X,Y) ? clear(X) ?
  gripping()) )

62
The format of the rules

• A ? (B ? C)
• where, A is the operator
• B is the result of the operation
• C is the conditions that must be true in
  order for the operator to be executable
• They tell what changes when the operator is
  executed (or applied.)

63
Portion of the search space or the blocks world
example
64
But ...

• We have no explicit notion of a state that
  changes over time as actions are performed.
• Remember that predicate logic is timeless,
  everything refers to the same time.
• In order to work reasoning about actions into
  logic, we need a way to tell that changes are
  happening over discrete times (or situations.)

65
Situation calculus

• We need to add an additional parameter which
  represents the state. Well use s0, , sn to
  represent states (aka situations).
• Now we can say
• 4. (?X) pickup(X, s0) ? (gripping(X, s1 )
  ? (gripping( , s0) ? clear(X, s0) ?
  ontable(X, s0)))
• If the pickup action was attempted in state 0,
  with the conditions listed holding, then in state
  1, gripping will be true for X.

66
Introduce holds and result and generalize
over states

• 4. (?X) (?s) (holds (gripping( ), s) ? holds
  (clear(X), s) ? holds (ontable(X), s) ) ?
  (holds(gripping(X), result(pickup(X),s))
• Using rules like this we can logically prove what
  happens as several actions are applied
  consecutively.
• Notice that gripping, clear, , are now
  functions.
• Is result a function or a predicate?

67
A small plan
c
c
b
b
a
a
(result(stack(c,b), (result( pickup(c),
(result (stack(b, a), (result(pickup(b),
(result(putdown(c),
(result(unstack(c,b),s0 ))))))
68
Our rules will still not work, because...

• We are making an implicit (but big) assumption
  we are assuming that if nothing tells us that p
  has changed, then p has not changed.
• This is important because we want to reason about
  change, as well as no-change.
• For instance, block a is still clear after we
  move block c around (except on top of block a).
• Things are going to start to get messier because
  we now need frame axioms.

69
A frame axiom

• Tells what doesnt change when an action is
  performed.
• For instance, if Y is unstacked from Z, nothing
  happens to X.
• (? X) (?Y) (?Z) (?s) (holds (ontable(X), s)
  ? (holds(ontable(X), result(unstack(Y, Z), s)
• For our logic system to work, well have to
  define such an axiom for each action and for each
  predicate.
• This is called the frame problem.
• Perhaps the time to get un-logical.

70
The STRIPS representation

• No frame problem.
• Special purpose representation.
• An operator is defined in terms of its
• name, parameters, preconditions, and results.
• A planner is a special purpose algorithm rather
  than a general purpose logic theorem
  prover forward or backward chaining (state
  space), plan space algorithms, and several
  significant others including
  logic-based.

71
Four operators for the blocks world

• P gripping() ? clear(X) ? ontable(X)
• pickup(X) A gripping(X)
• D ontable(X) ? gripping()
• P gripping(X)
• putdown(X) A ontable(X) ? gripping() ? clear(X)
• D gripping(X)
• P gripping(X) ? clear(Y)
• stack(X,Y) A on(X,Y) ? gripping() ? clear(X)
• D gripping(X) ? clear(Y)
• P gripping() ? clear(X) ? on(X,Y)
• unstack(X,Y) A gripping(X) ? clear(Y)
• D on(X,Y) ? gripping()

72
Notice the simplification

• Preconditions, add lists, and delete lists are
  all conjunctions. We no more have the full power
  of predicate logic.
• The same applies to goals. Goals are conjunctions
  of predicates.
• A detail
• Why do we have two operators for picking up
  (pickup and unstack), and two for putting down
  (putdown and stack)?

73
A goal state for the blocks world
74
A state space algorithm for STRIPS operators

• Search the space of situations (or states). This
  means each node in the search tree is a state.
• The root of the tree is the start state.
• Operators are the means of transition from each
  node to its children.
• The goal test involves seeing if the set of goals
  is a subset of the current situation.
• Why is the frame problem no more a problem?

75
Now, the following graph makes much more sense
76
Problems in representation

• Frame problem List everything that does not
  change. It no more is a significant problem
  because what is not listed as changing (via the
  add and delete lists) is assumed to be not
  changing.
• Qualification problem Can we list every
  precondition for an action? For instance, in
  order for PICKUP to work, the block should not be
  glued to the table, it should not be nailed to
  the table,
• It still is a problem. A partial solution is to
  prioritize preconditions, i.e., separate out the
  preconditions that are worth achieving.

77
Problems in representation (contd)

• Ramification problem Can we list every result of
  an action? For instance, if a block is picked up
  its shadow changes location, the weight on the
  table decreases, ...
• It still is a problem. A partial solution is to
  code rules so that inferences can be made. For
  instance, allow rules to calculate where the
  shadow would be, given the positions of the light
  source and the object. When the position of the
  object changes, its shadow changes too.

78
The gripper domain

• The agent is a robot with two grippers (left and
  right)
• There are two rooms (rooma and roomb)
• There are a number of balls in each room
• Operators
• PICK
• DROP
• MOVE

79
A deterministic plan

• Pick ball1 rooma right
• Move rooma roomb
• Drop ball1 roomb right
• Remember no observability, nothing can go wrong.

80
The domain definition for the gripper domain

• (define (domain gripper-strips) (predicates
  (room ?r) (ball ?b) (gripper ?g) (at-robby
  ?r) (at ?b ?r) (free ?g) (carry ?o ?g))
• (action move parameters (?from
  ?to) precondition (and (room ?from) (room
  ?to) (at-robby ?from)) effect
  (and (at-robby ?to) (not (at-robby ?from))))

name of the domain
? indicates a variable
combined add and delete lists
81
The domain definition for the gripper domain
(contd)

• (action pick parameters (?obj ?room
  ?gripper) precondition (and (ball ?obj) (room
  ?room) (gripper ?gripper) (at ?obj
  ?room) (at-robby ?room) (free
  ?gripper)) effect (and (carry ?obj ?gripper)
  (not (at ?obj ?room)) (not (free
  ?gripper))))

82
The domain definition for the gripper domain
(contd)

• (action drop parameters (?obj ?room
  ?gripper) precondition (and (ball ?obj) (room
  ?room) (gripper ?gripper) (at-robby
  ?room) (carrying ?obj
  ?gripper)) effect (and (at ?obj ?room) (free
  ?gripper) (not (carry ?obj
  ?gripper))))))

83
An example problem definition for the gripper
domain

• (define (problem strips-gripper2) (domain
  gripper-strips) (objects rooma roomb ball1
  ball2 left right) (init (room rooma) (room
  roomb) (ball ball1) (ball ball2) (gripper
  left) (gripper right) (at-robby rooma) (free
  left) (free right) (at ball1 rooma) (at ball2
  rooma) ) (goal (at ball1 roomb)))

84
Running VHPOP

• Once the domain and problem definitions are in
  files gripper-domain.pddl and gripper-2.pddl
  respectively, the following command runs Vhpop
• vhpop gripper-domain.pddl gripper-2.pddl
• The output will be
• strips-gripper2 1(pick ball1 rooma
  right) 2(move rooma roomb) 3(drop ball1 roomb
  right) Time 0
• pddl is the planning domain definition language.

85
Why is planning a hard problem?

• It is due to the large branching factor and the
  overwhelming number of possibilities.
• There is usually no way to separate out the
  relevant operators. Take the previous example,
  and imagine that there are 100 balls, just two
  rooms, and two grippers. Again, the goal is to
  take 1 ball to the other room.
• How many PICK operators are possible in the
  initial situation?
• pick parameters (?obj ?room ?gripper)
• That is only one part of the branching factor,
  the robot could also move without picking up
  anything.

86
Why is planning a hard problem? (contd)

• Also, goal interactions is a major problem. In
  planning, goal-directed search seems to make much
  more sense, but unfortunately cannot address the
  exponential explosion. This time, the branching
  factor increases due to the many ways of
  resolving interactions.
• When subgoals are compatible, i.e., they do not
  interact, they are said to be linear ( or
  independent, or serializable).

87
How to deal with the exponential explosion?

• Use goal-directed algorithms
• Use domain-independent heuristics
• Use domain-dependent heuristics (need a language
  to specify them)

88
The monkey and bananas problem
89
The monkey and bananas problem (contd)

• The problem statement A monkey is in a
  laboratory room containing a box, a knife and a
  bunch of bananas. The bananas are hanging from
  the ceiling out of the reach of the monkey. How
  can the monkey obtain the bananas?

?
90
VHPOP coding

• (define (domain monkey-domain) (requirements
  equality) (constants monkey box knife glass
  water waterfountain) (predicates
  (on-floor) (at ?x ?y) (onbox ?x) (hasknife)
  (hasbananas) (hasglass) (haswater) (location
  ?x) (action go-to parameters (?x ?y)
  precondition (and (not ?y ?x)) (on-floor)
  (at monkey ?y) effect (and (at monkey ?x)
  (not (at monkey ?y))))

91
VHPOP coding (contd)

• (action climb parameters (?x)
  precondition (and (at box ?x) (at monkey ?x))
  effect (and (onbox ?x) (not (on-floor))))
• (action push-box parameters (?x ?y)
  precondition (and (not ( ?y ?x)) (at box ?y)
  (at monkey ?y) (on-floor)) effect (and
  (at monkey ?x) (not (at monkey ?y)) (at box
  ?x) (not (at box ?y))))

92
VHPOP coding (contd)

• (action getknife parameters (?y)
  precondition (and (at knife ?y) (at monkey ?y))
  effect (and (hasknife) (not (at knife ?y))))
• (action grabbananas parameters (?y)
  precondition (and (hasknife) (at bananas ?y)
  (onbox ?y) ) effect (hasbananas))

93
VHPOP coding (contd)

• (action pickglass parameters (?y)
  precondition (and (at glass ?y) (at monkey ?y))
  effect (and (hasglass) (not (at glass ?y))))
• (action getwater parameters (?y)
  precondition (and (hasglass) (at waterfountain
  ?y) (ay monkey ?y) (onbox ?y)) effect
  (haswater))

94
Problem 1 monkey-test1.pddl

• (define (problem monkey-test1) (domain
  monkey-domain) (objects p1 p2 p3 p4) (init
  (location p1) (location p2) (location p3)
  (location p4) (at monkey p1) (on-floor) (at box
  p2) (at bananas p3) (at knife p4)) (goal
  (hasbananas)))
• go-to p4 p1get-knife p4go-to p2 p4push-box p3
  p2climb p3grab-bananas p3 time 30 msec.

95
Problem 2 monkey-test2.pddl

• (define (problem monkey-test2) (domain
  monkey-domain) (objects p1 p2 p3 p4 p6)
  (init (location p1) (location p2) (location
  p3) (location p4) (location p6) (at monkey p1)
  (on-floor) (at box p2) (at bananas p3) (at knife
  p4) (at waterfountain p3) (at glass p6))
  (goal (and (hasbananas) (haswater))))
• go-to p4 p1 go-to p2 p6 get-knife
  p4 push-box p3 p2go-to p6 p4 climb
  p3pickglass p6 getwater p3 grab-bananas
  p3 time 70 msec.

96
The monkey and bananas problem (contd)
(Russell Norvig, 2003)

• Suppose that the monkey wants to fool the
  scientists, who are off to tea, by grabbing the
  bananas, but leaving the box in its original
  place. Can this goal be solved by a STRIPS-style
  system?

97
Triangle table (execution monitoring and macro
operators)
98
Teleo-reactive planning combines feedback-based
control and discrete actions (Klein et al., 2000)
99
Model-based reactive configuration management
(Williams and Nayak, 1996a)

• Intelligent space probes that autonomously
  explore the solar system.
• The spacecraft needs to
• radically reconfigure its control regime in
  response to failures,
• plan around these failures during its remaining
  flight.

100
A schematic of the simplified Livingstone
propulsion system (Williams and Nayak ,1996)
101
A model-based configuration management system
(Williams and Nayak, 1996)
ME mode estimation MR mode
reconfiguration
102
The transition system model of a valve
(Williams and Nayak, 1996a)
103
Mode estimation (Williams and Nayak, 1996a)
104
Mode reconfiguration (MR)(Williams and Nayak,
1996a)
105
Comments on planning

• It is a synthesis task
• Classical planning is based on the assumptions
  of a deterministic and static environment
• Algorithms to solve planning problems include
• forward chaining heuristic search in state space
• Graphplan mutual exclusion reasoning using plan
  graphs
• Partial order planning (POP) goal directed
  search in plan space
• Satifiability based planning Convert problem
  into logic
• Non-classical planners include
• probabilistic planners
• contingency planners (aka conditional planners)
• decision-theoretic planners
• temporal planners