程序代写代做代考 prolog AI Introduction to AI Knowledge Representation and Reasoning
Introduction to AI Knowledge Representation and Reasoning
Introduction to AI
Some applications of KRR
Francesca Toni
(thanks to Fariba Sadri)
Outline
• Logical agents
• Logic-based Production System
• (Rule-based robotics)
• Ontologies
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Section 7.1
Section 12.5
Section 2.4
Section 14.3
Logical agents
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Perceive
Reason and
choose action(s)
Execute action(s)
inference engine
knowledge base Domain-dependent, dynamic – e.g. logic program
Domain-independent/static – e.g. SLDNF/(XSB)Prolog
goal(s) Domain-dependent, dynamic
LPS (Logic-based Production System)
http://lpsdemo.interprolog.com/
maxTime( ).
fluents … .
actions … .
events … .
Declarations
Inputs
Reactive rules
Causal theory
Clauses
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initially … .
observe … .
if … then … .
… if … .
… :- … .
… initiates … if … .
… terminates … if … .
false … .
% Fire example
% Declarations
maxTime(5).
fluents fire.
actions eliminate, escape.
events deal_with_fire.
% Initial state: Inputs
initially fire.
% Goals: Reactive Rules
if fire at T1 then deal-with-fire from T1 to T2.
% Beliefs: Clauses
deal-with-fire from T1 to T2 if eliminate from T1 to T2.
deal-with-fire from T1 to T2 if escape from T1 to T2.
% Causal theory
eliminate terminates fire.
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% Fire example extended with recurrent fires
% Declarations
maxTime(10).
fluents fire, water.
actions eliminate, escape, ignite(_), refill.
events deal_with_fire.
% Initial state: Inputs
initially water.
% Observations: Inputs
observe ignite(sofa) from 1 to 2.
observe ignite(bed) from 4 to 5.
observe refill from 7 to 8.
% Goals: Reactive Rules
if fire at T1 then deal-with-fire from T2 to T3.
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% Beliefs: Clauses
deal-with-fire from T1 to T2
if eliminate from T1 to T2.
deal-with-fire from T1 to T2
if escape from T1 to T2.
% Time-independent information: Clauses
flammable(sofa).
flammable(bed).
% Causal theory
ignite(Object) initiates fire
if flammable(Object).
eliminate terminates fire.
eliminate terminates water.
refill initiates water.
false eliminate, fire, not water.
% Planning in the blocks’ world
maxTime(10).
fluents location(_,_).
actions move(_,_).
initially location(f,floor), location(b,f),location(e,b),
location(a,floor), location(d,a),location(c,d).
% Goals
if true
then make_tower([a,b,c,floor]) from T1 to T2.
if true
then make_tower([f,e,d,floor]) from T1 to T2.
clear(Block) at T if Block = floor,
not location(_,Block) at T.
clear(floor) at _.
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make_tower([Block,floor]) from T1 to T2 if
make_on(Block,floor) from T1 to T2.
make_tower([Block,Place|Places]) from T1 to T3 if
Place = floor,
make_tower([Place|Places]) from T1 to T2,
make_on(Block,Place) from T2 to T3.
make_on(Block,Place) from T1 to T4 if
not location(Block,Place) at T1,
make_clear(Place) from T1 to T2,
make_clear(Block) from T2 to T3,
move(Block,Place) from T3 to T4.
make_on(Block,Place) from T to T if
location(Block,Place) at T.
make_clear(Place) from T to T if clear(Place) at T.
make_clear(Block) from T1 to T2if
location(Block1,Block) at T1,
make_on(Block1,floor) from T1 to T2.
move(Block,Place) initiates location(Block,Place).
move(Block,_) terminates location(Block,Place).
Rule-based systems for robotics (1)
-non-examinable-
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A Framework for Integrating Symbolic
and Sub-symbolic Representations
a robot building towers of blocks, subject to human interference, using
1) a concurrent multi-tasking teleo-reactive program,
2) a physics simulator to provide spatial knowledge,
3) sensor processing and robot control.
Rule-based systems for robotics (2)
-non-examinable-
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Representations for robot knowledge
in the KnowRob framework
https://ac.els-cdn.com/S0004370215000843/1-s2.0-S0004370215000843-main.pdf?_tid=2dbd26aa-0bfe-11e8-8bb8-00000aacb35e&acdnat=1518004875_34e79120b90c95bdca71b117509c1c8a
Rule-based systems for robotics (2)
Semantic environment maps
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Map of a kitchen including trajectories
for opening the different cupboards.
Ontology: representation of composed objects and
kinematic structures with prismatic and rotational joints
Ontologies
• Explicit and formal specification of a conceptualization …“the
kind of things that exist in a given domain”
• Typical Components of Ontologies
– Concepts of the domain
• e.g. classes of objects
– professors, students, courses
– Relationships between these terms:
• e.g. class hierarchies: a class C to be a subclass of another
class C’ if every object in C is also included in C’
– all professors are staff members
• On the web: ontologies provide a shared understanding of a
domain: semantic interoperability
– overcome differences in terminology
– map between ontologies
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RDF (Resource Description Framework):
Universal language for describing resources:
• Statements of the form object-attribute-value assert
properties of objects (resources):
– they consist of an object (resource), a property, and a value
• e.g. (http://www.x.y/~ft, http://www.k.z/site-owner, #ft)
• Can be also seen as
– a (piece of a) graph (known in AI as a semantic net)
– a piece of XML code
– an atom: site-owner(http://www.x.y/~ft, #ft)
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http://www.x.y/~ft #ft
site-owner
RDF schema
• Classes (and properties)
– type(a,C) states that a is instance of class C
• Class Hierarchies (and Inheritance)
– subClassOf(C,D) states that class C is a subclass of
class D
• Property Hierarchies
– subPropertyOf(P,Q) states that property Q is true
whenever property P is true
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RDF and RDF Schema in positive logic programming
(description logic programming)
• A triple of the form (a,P,b) in RDF can be expressed as a
fact P(a,b)
– E.g. isTaughtBy(introAI,ft).
• An instance declaration of the form type(a,C) (stating a
is instance of class C) can be expressed as C(a)
– E.g. professor(ft)
• The fact that A is a subclass (or subproperty) of B can
be expressed as A(X) B(X)
– E.g. academicStaffMember(X)<-professor(X)
(involves(X,Y) <- isTaughtBy(X,Y) )
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Summary
• Knowledge representation (and automated
reasoning) for
– Logical agents (and rule-based robots)
– Ontologies in RDF and RDF schema - for robots
and the web
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