Artificial Intelligence Knowledge engineering

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Artificial IntelligenceKnowledge engineering

• Fall 2008
• professor Luigi Ceccaroni

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Knowledge engineering

• A knowledge engineer is someone who
• Investigates a particular domain
• Learns what concepts are important in that domain
• Creates a formal representation of the objects
  and relations in the domain
• Knowledge engineering is a process for developing
  special-purpose knowledge bases
• whose domain is carefully circumscribed and
• whose range of queries is known in advance.

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The software engineering process waterfall model
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The software engineering process spiral model
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The knowledge engineering process general
methodology
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Identify the task

• Feasibility of the construction of the KBS
• Search for the sources of knowledge (experts,
  books, Internet)
• Specification of necessary data to solve the
  problem
• Specification of the goals (solutions) or of the
  criteria which define the solution

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Assemble the relevant knowledge

• Specification of the basic elements to
  characterize the domain (relevant facts) and
  their relations
• Distinction between evidences, hypotheses and
  actions to be carried out
• Specification of the different hypotheses and
  goals
• Decomposition of the problem into sub-problems
• Characterization of the reasoning flow

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Vocabulary of predicates, functions and constants

• Translation of the important domain-level
  concepts into logic-level names
• Questions of knowledge engineering style
• Should pits be independent objects or a unary
  predicate on square objects?
• Should the agents orientation be a function or
  a predicate?
• Should the agents location depend on time?
• Once the choices have been made, the result is a
  vocabulary that is known as the ontology of the
  domain.

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Formalize and encode the knowledge about the
domain

• Determination of the necessary reasoning schemas
• classification
• diagnosis
• temporal planning
• causal structures
• Identification of the search space and the type
  of search
• Identification of the resolution methodology
• heuristic classification
• constructive problem-solving
• hierarchical hypothesize and test

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Formalize and encode the knowledge about the
domain

• Analysis of inaccuracy (uncertainty, imprecision)
  and completeness
• Implementation of the representation
• Facts base
• Modular structure of the knowledge base
• Inference rules of each module
• Decisions on the control of the resolution
• Meta-rules associated to the modules
• Other meta-rules

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Encode a description of the specific problem
instance

• Problem instances are supplied by the sensors or,
  in general, they are generic input data with a
  certain structure and semantics.
• It involves writing simple atomic sentences about
  instances of concepts that are already part of
  the ontology.

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Pose queries to the inference procedure and get
answers

• Let the inference procedure operate on the rules,
  axioms and problem-specific facts to derive the
  facts we are interested in knowing.

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Debug the knowledge base

• Specification of a set of test cases
• Evaluation of the functioning of the system
  (prototype)
• accuracy
• completeness
• credibility (explanations)

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Types of KBSs (Clancey, 1985)

• Analysis tasks interpretation of a system
• control
• prediction (simulation)
• identification (monitoring, diagnosis)
• Synthesis tasks construction of a system
• design (planning, configuration)
• assemblage (modification)
• specification

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Analysis-oriented KBSs

• Diagnosis oriented
• Medical diagnosis, failures diagnosis
• Classification oriented
• User profiling, species identification
• Supervision oriented
• Real-time process supervision
• Prediction oriented
• Meteorology, stock-market prediction

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Synthesis-oriented KBSs

• Planning oriented
• Robot course planning
• Design oriented
• Architectural design
• Configuration oriented
• Computer networks configuration

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Practical problem-solving methods

• Heuristic classification
• Constructive problem-solving
• Helping expert-systems builders to make the right
  design decisions with respect to the methods and
  representations most suitable for their
  application will do much to prevent the
  frustration and disillusion that often accompany
  bad decisions. (Peter Jackson, Introduction to
  expert systems, 1990)

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Heuristic classification

• Heuristic classification has been identified as a
  widespread problem solving method.
• It is comprised of three main phases
• abstraction from a concrete, particular problem
  description to a problem class
• heuristic match of a general solution (method) to
  the problem class
• refinement of the general solution to a concrete
  solution for the concrete problem
• To be applied, a finite set of solutions needs to
  be identified a priori.

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Heuristic classification

• HC can be applied in analysis tasks
• classification
• diagnosis
• identification
• monitoring
• ...
• It is used for complex problems.
• If the problem is simple, a direct association
  between data and solutions is enough.

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Heuristic classification
heuristic association
refinement and adaptation
data abstraction
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Heuristic classification

• Data abstraction
• Data of the specific case are abstracted to
  obtain a more general case
• Types of abstraction/generalization
• Based on taxonomy
• From quantitative to qualitative measures
• Temperature de Q 38 ºC
• If Temperature gt 37.5 ºC then Temperature is high

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Heuristic classification

• Heuristic association (matching)
• Determining the relation between abstract cases
  and abstract solutions
• If Temperature is high then has-fever
• Refinement and adaptation of the solution
• Identifying specific solutions from abstract
  solutions and complementary data
• If has-fever and other data then
• Q has flu

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Heuristic classification example
MYCIN
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Heuristic classification example

• Concesión de créditos para fundar una nueva
  empresa
• Atributos (ejemplos)
• Apoyo financiero (tiene avales, es-rico...)
• Petición (106 ...)
• Bienes (cuentas-corrientes, casas, coches,
  yates...)
• Fiabilidad-de-la-devolución (morosidad,
  cheques-sin-fondos...)
• Compromiso (créditos-anteriores...)
• Soluciones
• Denegación
• Aceptación
• Aceptación con rebaja
• Aceptación con interés preferente

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Heuristic classification example

• Reglas de abstracción
• Bienes lt 10 petición ? Bienes insuficientes
• Bienes 10 petición ? Bienes lt 20 petición ?
  Bienes suficientes
• Bienes 20 petición ? Bienes excelentes
• Avales 10 petición ? Es-rico ?
  Apoyo-financiero bueno
• Avales lt 10 petición ? Avales petición ?
  Apoyo-financiero
• moderado
• Avales lt petición ? Apoyo-financiero bajo
• Cheques-sin-fondos ? Moroso ? Fiabilidad-de-la-dev
  olución baja
• Empresa es churrería ? Empresa es tienda de roba
  ? Viabilidad buena
• Empresa es hamburguesería cerca de universidad ?
  Viabilidad buena
• Empresa es Corte-Inglés ? Empresa es proveedor
  Internet vía cable ?
• viabilidad muy buena
• Crédito lt petición ? Compromiso bajo
• Crédito petición ? Crédito lt 10 petición ?
  Compromiso mediano
• Crédito 10 petición ? Compromiso alto

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Heuristic classification example

• Reglas de asociación heurística
• Apoyo-financiero bajo ? Bienes insuficientes ?
  Denegación
• Fiabilidad-de-la-devolución baja ? Denegación
• . . .
• Apoyo-financiero moderado ? Bienes suficientes ?
• Viabilidad buena ? Aceptación con rebaja
• . . .
• Apoyo-financiero bueno ? Bienes suficientes ?
  Compromiso mediano
• ? Viabilidad buena ? Aceptación
• . . .
• Apoyo-financiero bueno ? Bienes excelentes ?
  Compromiso alto ?
• Viabilidad muy buena ? Aceptación con interés
  preferente
• . . .

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Heuristic classification example

• Regles de refinamiento/adaptación de las
  soluciones
• Aceptación con rebaja ? Petición lt 107 ? Bienes lt
  5 Petición ?
• Rebaja a 0.6 Petición
• . . .
• Aceptación con interés preferente ? Petición
  107 ? Bienes 10 Petición ? Interés
  preferente 1 inferior al del mercado

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Heuristic classification example
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Constructive problem-solving

• Solutions can be infinite and do not need to be
  identified a priori.
• Solutions are constructed and not selected among
  various possibilities.
• Constructive problem-solving is applicable in
  synthesis tasks
• planning
• design
• ...

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Constructive problem-solving

• Solutions are combinations of certain elements,
  which satisfy some constraints
• Planning
• Elements are actions.
• Solutions are sequences of actions accomplishing
  a certain goal.
• Design
• Elements are components.
• Solutions are combinations of components forming
  a complex object.

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Constructive problem-solving

• The construction of the solution implies knowing
• a model of the structure of the complex object
• a model of the behavior of the complex object
• a set of constraints on the complex object

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Constructive problem-solving

• Constraints can be
• On the configuration of the components of the
  solution
• Physical/spatial constraints
• How to hold an object
• An object cannot be placed in a certain place
• Temporal constraints
• Which action is carried out first
• On the pre- and post-conditions of
  operator/actions
• On the interaction between the previous ones

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Constructive problem-solving example 1

• Planning of the (optimal) path of a robot exit a
  room with obstacles
• Operators/actions
• go forward (m) turn (degrees) go backward (m)
• Constraints
• The robot cannot touch any obstacle.
• At the and the robot has to be at the exit.
• Only the movements indicated by the operators are
  allowed.

R
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Constructive problem-solving example 2

• Configure/place a set of furniture/objects in a
  room
• Operators/actions
• place-furniture (furniture, position)
  remove-furniture (furniture) swap-furniture
  (furniture-1, furniture-2) move-furniture
  (furniture, position)
• Constraints
• Doors and windows cannot be blocked
• All furniture has to be placed
• Only the operations indicated by the operators
  are allowed.

Wii
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Sofa
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Constructive problem-solving sub-methods

• Propose and apply
• The problem needs to be decomposed into tasks
  (sub-problems) with clear spatial/temporal
  relations among them.
• Operations, with their constraints and effects,
  need to be clearly defined.
• Least commitment
• Partial solutions are needed to start, and are
  then improved to get to the final solution.

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Propose and apply

• The method starts from an empty or incomplete
  initial state.
• Each step contributes to the completion of the
  solution.
• The best operator is chosen at each step.

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Propose and apply

• Exhaustive knowledge is needed about
• Operators, and their effect on the solution
• Constraints and relations among components of the
  solution
• Quality of the solution
• Resolution can be through
• Sequential construction (It needs a lot of
  knowledge to be efficient.)
• Hierarchical task-decomposition (It is more
  efficient, but needs decomposition operators.)

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Propose and apply resolution process

1. Goal initialization (task to be carried out)
   necessary elements to identify the initial state
   are created.
2. Operator proposal all operators that can actuate
   on the current state are considered.
3. Operator elimination some operators are
   eliminated according to global criteria (e.g.,
   predefined preference order).
4. Operator evaluation the effects of the operators
   on the solution are compared using expert
   knowledge.
5. Operator selection the operator with the best
   evaluation is selected.
6. Operator application the selected operator is
   applied to the current state.
7. Goal evaluation if the goal is reached, the
   process stops, otherwise it restarts from step 2.

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Least commitment

• The method starts from a complete state.
• At each step, the state is modified/improved/corr
  ected.
• The operator to be applied is defined by the
  least commitment strategy
• The modification that imposes less future
  constraints.

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Least commitment resolution process

1. Start with a complete, non optimal state, which
   satisfies the constraints.
2. Modify the state applying the least commitment
   heuristics Choose the operator that imposes
   less constraints on future actions.
3. If the modification violates any constraints,
   then undo some of the previous steps, trying to
   minimize the undoing modifications.
4. If the goal is reached, the process stops,
   otherwise it restarts from step 2.

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Least commitment example

• Queremos planificar la mejor trayectoria de un
  robot en una habitación
• La habitación tiene un conjunto de obstáculos que
  queremos evitar
• Disponemos de un conjunto de operadores
• Movernos hacia adelante o hacia atrás a cierta
  velocidad y cierta distancia
• Girar cierto número de grados

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Least commitment example
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Least commitment example

• Restricciones globales llegar a la puerta de
  salida, trayectoria mínima en recorrido y tiempo
• Restricciones de elección de operadores No
  chocar con obstáculos o la pared, mantener la
  distancia para poder maniobrar
• Evaluación de los operadores
• Mover Mejor cuanto más lejos y más deprisa nos
  lleve al objetivo
• Girar Mejor cuanto más lejos deje los obstáculos
  de nuestra trayectoria

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Hierarchical hypothesize and test

• La formación de hipótesis y pruebas organizadas
  jerárquicamente (HPJ) combina aspectos de
  clasificación heurística y de resolución
  constructiva de problemas.
• Es indicada en problemas donde
• El espacio de soluciones posibles es muy grande,
  pero estas toman valores en un dominio finito.
• El espacio de hipótesis (nodos de la resolución)
  está organizado jerárquicamente
• Los nodos altos corresponden a hipótesis más
  generales, que se van refinando hasta llegar a
  las hojas que corresponden a hipótesis más
  concretas.
• La estructuración jerárquica ayuda a plantear el
  problema y facilita la solución.
• Ejemplos
• CENTAUR (Aikins, 1983)
• INTERNIST (Pople, 1977)
• TEST (Kahn et al., 1987)

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Hierarchical hypothesize and test resolution
process

• 1. Leer los datos iniciales del problema y
  formular hipótesis.
• 2. Asignar a cada hipótesis una puntuación que
  refleje la proporción de los datos explicados.
• 3. Determinar el mejor nodo según la puntuación
  n.
• 4. Si nodo-(n)-es-solución entonces acabar
• si no dividir el espacio de
  hipótesis en 2 conjuntos K i L
• K lt-- sucesores de n
• L lt-- competidores en
  espera de n
• 5. Recoger más datos que discriminen entre las
  hipótesis de K y puntuar-les.
• 6. Sean k lt-- mejor (K) y l lt-- mejor (L)
• 7. Si puntuación (k) gt puntuación (l) entonces n
  lt-- k si no n lt-- l
• 8. Tornar a 4

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Hierarchical hypothesize and test example

• A hierarchical representation of lung diseases in
  CENTAUR