Plan Representation and Reasoning with Description Logics and Ontologies

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Plan Representation and Reasoningwith
Description Logicsand Ontologies

• Yolanda Gil
• Lecture Notes, October 4, 2000
• CS 541 Artificial Intelligence Planning
• www.isi.edu/gil/cs541

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Outline

• Representing actions and plans with description
  logic
• Action taxonomies (CLASP)
• Plan taxonomies (SUDO-PLANNER)
• Goal taxonomies (EXPECT)
• Planning ontologies
• Process Specification Language (PSL), NIST
• PLANET

3
Representing Knowledge in Description Logic (DL)

• Description logics are extensions of frame-based
  systems where classes can be defined
  intensionally
• Ex SUVs are vehicles with 4 seats that weight
  between 1T and 2T
• Class taxonomy is automatically generated through
  subsumption
• A subsumes B iff all instances of B are also
  instances of A
• Instances can be automatically classified
• Ex MyNewCar is a vehicle with 4 seats that
  weighs 1.3T
• gt MyNewCar is an SUV
• Relations can also have definitions and can be
  classified
• Tradeoff between expressivity and efficient
  reasoning
• Some well know description logic systems
  CLASSIC, LOOM, NIKL

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Representing Planning Knowledge in Description
Logics Overview

• Action taxonomies in CLASP
• extended language to represent action networks
• Plan taxonomies in SUDO-PLANNER
• plan subsumption of partially ordered plans
• Goal taxonomies in EXPECT
• expressive representations of goals and their
  parameters
• These systems can exploit the descriptions of all
  the objects in the domain (domain knowledge) in
  order to reason about action, goal, and plan
  descriptions

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CLASP CLAssification of Scenarios and Plans
Devanbu and Litman 94

• Extension of a DL system (CLASSIC)
• Language to express action networks
• Sequence, loop, repeat, test, subplan
• Subsumption and classification algorithms for
  that language
• Action network subsumption viewed as DFA
  acceptance
• Propositional, STRIPS-style representation of
  actions
• States (goals are represented as states)
• Actions
• Plans
• Scenarios (plan instances)
• Reasoning based on these descriptions
• Organizing plan classes
• Retrieving plan types
• Validation of scenarios

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Core Classes in CLASP
(DEFINE-CONCEPT Action (PRIMITIVE (AND
Classic-Thing (AT-LEAST 1 Actor) (ALL ACTOR
Agent) (EXACTLY 1 PRECONDITION) (ALL
PRECONDITION State) (EXACTLY 1 ADD-LIST) (ALL
ADD-LIST State) (EXACTLY 1 DELETE-LIST) (ALL
DELETE-LIST State) (EXACTLY 1 GOAL)
(ALL GOAL STATE))))
(DEFINE-CONCEPT State (PRIMITIVE
Classic-Thing)) (DEFINE-PLAN Plan
(PRIMITIVE (AND Clasp-Thing (EXACTLY 1
INITIAL) (ALL INITIAL State)
(EXACTLY 1 GOAL) (ALL GOAL State)
(EXACTLY 1 PLAN-EXPRESSION) (ALL
PLAN-EXPRESSION (LOOP Action)))))
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Defining Actions, States and Plans in CLASP in a
Telephony Domain
(DEFINE-PLAN Pots-Plan (AND Plan (ALL
PLAN-EXPRESSION (SEQUENCE (SUBPLAN
Originate-And-Dial-Plan) (TEST
(Callee-On-Hook-State (SUBPLAN
Terminate-Plan)) (Callee-Off-Hook-State
(SEQUENCE Non-Terminate-Act
Caller-On-Hook-Act Disconnect
Act))))))) (DEFINE-PLAN Originate-And-Dial-
Plan (AND Plan (ALL PLAN-EXPRESSION (SE
QUENCE Caller-Off-Hook-Act
Connect-Dialtone-Act Dial-Digits-Act))))
(DEFINE-CONCEPT System-Act (AND Action
(ALL ACTOR System-Agent))) (DEFINE-CONCEPT
Connect-Dialtone-Act (AND System-Act (ALL
PRECONDITION (AND Off-Hook-State
Idle-State)) (All Add-LIST
Dialtone-State) (ALL DELETE-LIST Idle-State
(ALL GOAL (AND Off-Hook-State
Dialtone-State)))) (DEFINE-CONCEPT
Callee-Off-Hook-State (PRIMITIVE
State)) (DEFINE-CONCEPT Callee-On-Hook-State
(PRIMITIVE State)) (DEFINE-CONCEPT
Callee-Off-Caller-On-State (AND
Callee-Off-Hook-State Caller-On-Hook-State))
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Defining Instances in CLASP
(CREATE SCENARIO pots-busy-scenario (AND
Plan (FILLS INITIAL state-u1on-u2off)
(FILLS GOAL state-u1on) (FILLS
PLAN-EXPRESSION (caller-off-hook-u1
connect-dialtone-on-u1 dial-digits-u1-to-u2
non-terminate-on-u2 caller-on-hook-u1
disconnect-u1))))
(CREATE-IND state-u1on-u2off (AND state-U1on
State-U2off)) (CREATE-IND connect-dialtone-on
-u1 (AND Connect-Dialtone-Act (FILLS
ACTOR switching-system) (FILLS
PRECONDITION state-u1off-idle)))
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SUDO-PLANNER Wellman 88

• Exploits subsumption to control the search during
  plan generation
• Actions represented in DL (NIKL), organized in
  taxonomy
• Plans represented as partially ordered sets of
  actions
• Eliminate search nodes whose plan is subsumed
  (dominated) by other nodes
• SUDO-PLANNER had other features not discussed
  here
• Uncertainty reasoning and partial goal
  satisfaction
• Policy constraints that relate actions to
  external events
• Conditional effects
• Qualitative probabilistic networks

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Action Taxonomy in SUDO-PLANNER
(defconcept surgery is (and action
(the route invasive-path-into-body))) (defc
oncept biopsy is-primitive action
...)) (defconcept open-lung-biopsy is (and
biopsy (the route
open-lung-path))) (defconcept open-lung-path
is (and invasive-path-into-body ...)) System
deduces that open-lung-biopsy is a surgery
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Plan Representation and Subsumption in
SUDO-PLANNER

• Plan is described as a set of action types
  associated with identifiers
• (surgery, id1) (CABG, id2)
• Plan is simplified if action subsumption and same
  id
• (surgery, id1) (CABG, id1) -gt (surgery, id1)
  
• Plan subsumption
• Action network viewed as bipartite graph matching

a4
a2
a5
a5
a1
a1
a3
a4
a6
a6
a2
a3
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Eliminating Redundant Paths in Plan Space Search

• Dominance-based planning
• Generate new nodes by adding constraints to
  search nodes
• Derive dominance (i.e., subsumption) based on
  domain knowledge
• Eliminate nodes in the plan graph that are
  dominated by others

...
A a1 A
a2 A
A a1 b5 A
A aiaj ai subsumes aj when iltj
X
a1 b5 A
a2 b7 A
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Reasoning about Goals in EXPECT Swartout et al
98

• Highly declarative representation of goals
• Goals as verb-based expressions
• Rich language of goal parameter types
• Qualification parameters that describe the type
  of task
• Intentional and extensional sets
• Given a goal, matcher looks for methods
  (operators) that have the capability of achieving
  that goal
• can match variabilized goals
• can decompose goal into subgoals through
  reformulations
• Goal representations have been used in several
  contexts
• representing planning goals
• problem solving
• agent matchmaking

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Representing Goals in EXPECT

• Represented as a case grammar (verb roles)
• ex ESTIMATE OBJ duration OF trip
• Roles can be filled by
• a specific instance add OBJ 3 TO 5
• a concept compute OBJ (spec-of
  factorial) OF 7
• a type of instance divide OBJ number BY 2
• extensional sets find OBJ (spec-of
  maximum) OF (54 15 256)
• intensional sets add OBJ (set-of number)
• find OBJ
  (set-of (spec-of violated-constraint))
• 
  IN configuration
• Roles filled by concepts express task
  qualification parameters declaratively
• (compute-factorial ?n) -gt (compute (obj (spec-of
  factorial)) (of number)))

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Matching Goals in EXPECT

• Desired goals and available capabilities are
  automatically translated to LOOM concepts
• Classifier is used to find most specific method
  capability that subsumes the posted goal

OBJ
cargo
Method capability (move (OBJ (inst-of
cargo)) (WITH (inst-of aircraft)))
move
OBJ
WITH
cargo
vehicle
move
WITH
aircraft
Goal (move (OBJ (inst-of cargo))
(WITH C-140))
OBJ
cargo
OBJ
cargo
move
WITH
move
WITH
C-140
truck
Self-organizing method taxonomy
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Flexible Matching through Goal Reformulation

• When no capability matches a posted goal, but
  more specific versions of the goal match
• ex no method to estimate round-trip time (rtt)
  of a vehicle, but there are methods to estimate
  rtt of aircraft and trucks
• Use descriptive knowledge to reformulate goal
• reexpress goal into subgoals by breaking down one
  of the arguments
• recombine the results of solving subgoals
• Conjunctive (disjunctive) subgoals produce
  conjunctive (disjunctive) reformulations
• Types of reformulations
• Covering reformulation subgoals cover partitions
  of a class
• Set reformulation subgoals iterate over elements
  of a set
• Input reformulation subgoals handle each of the
  subtypes

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Goal Matching in EXPECT
Calculate round-trip time (RTT) for aircraft
Find route from location1 to location2
Calculate RTT for combat aircraft
Calculate RTT for transport aircraft
Find egress route from Ryad to Kuwait city
A) Subsumption-based match the posted goal is
subsumed by a capability
B) Reformulation-based match the posted goal can
be satisfied by combining two or more existing
capabilities
Find route from location1 to location2
Find phone numbers of US citizens in Kuwait
Find addresses of US citizens in Kuwait
Find route from city1 to city2
C) Reverse subsumption-based match a capability
can satisfy some aspect of the goal
D) Partial match a capability is similar/related
to the posted goal
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Overview of Planning Ontologies

• Why planning ontologies
• knowledge reuse
• knowledge sharing
• knowledge modeling
• Process descriptions in PSL
• temporal constraints
• resources
• Describing plans in PLANET
• can represent state-based, plan-based search,
  hierarchical plans
• captures plan representations understandable by
  people

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Process Specification Language (PSL) NIST, 99

• National Institute of Standards and Technology
  (NIST), Manufacturing Systems Division
• Academic and industrial collaborators
• Proposed to Intl Standards Organization (ISO)
• PSL core represents widely accepted commitments
• activity, activity-occurrence, object, timepoint
• PSL extensions accommodate possible shareable
  agreements
• Contains axioms defining terms and constraints
• Available at http//www.mel.nist.gov/psl/

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PSL Overview
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PSL Modules for Activities and Orderings
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PSL Core

• Activity
• Generic activity occurrences, interruptions,
  nondeterministic, subactivities
• Ordering ordering over activities, complex
  ordering relations, junctions
• Objects
• Resources capacities, homogeneous sets,
  inventories, divisibility, usage, resource paths,
  pools, requirements, resource roles,
  substitutability
• States defined, constraints
• Timepoints
• Duration theory, activity durations, temporal
  orderings

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PLANET a PLAN Semantic nET Gil Blythe 00

• Capture unifying views on planning algorithms
• constraints, commitments, task templates,
  alternative choices
• state-based and objective-based goals
• operator-based and HTN-based plans
• Represent manually created plans
• typically include unintended flaws (incomplete,
  unjustified, inconsistent)
• Capture planning context
• initial constraints (guide, user advice,
  preferences) and restraints
• initial state, constraints and goals may be
  incompatible
• Available from http//www.isi.edu/expect/projects/
  planet/

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Some Terms Defined in PLANET

• Planning problems
• Planning problem context world state, desired
  goals, external constraints
• Planning problem candidate plans (rejected,
  feasible, selected)
• Goals and effects
• Goals state-based goals, objective-based goals
• Human readable descriptions
• Actions, operators, and tasks
• Plan task descriptions plan task templates, plan
  tasks,
• Capabilities, preconditions, effects, subtasks,
  primitive tasks, plan steps
• Plans
• Commitments, sub-plans, planning level

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PLANET An Ontology for Representing Plans

capability
plan-commitments
Plan
effects
Plan-task-description
preconditions
plan-refinements
planning-level
sub-tasks
task-of
sub-plans
feasible
human-readable description
Plan-task-template
task-template
Plan-task
consistent
rejected
complete
accomplishes
ordering
feasible
justified
Goal-specification
planning - level
temporal
selected
commitments
candidate-plans
unexplored
state-based -goal-spec
objective-based -goal-spec
desired-goals
Planning-problem-context
initial-state
world-state
resource-needed
planning-problems
Resource-requirements
external constraints
amount
when-needed