Plan Representation and Reasoning with Description Logics - - PowerPoint PPT Presentation

SLIDE 1

Plan Representation and Reasoning with Description Logics

SLIDE 2

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

• bjects in the domain (domain knowledge) in
• rder to reason about action, goal, and plan

descriptions

SLIDE 3

Defining Actions, States and Plans in CLASP in a Telephony Domain

(DEF INE-PLAN Po ts

• P

lan (AND P lan (ALL PLAN-EXPRESSION (SEQUENCE (SUBPLAN O r i g i nat e

• And
• D

ia l

• P

lan ) (TEST (Ca l l ee

• On-Hook
• St

a t e (SUBPLAN Te rm ina te

• P

lan ) ) (Ca l l ee

• O

f f

• Hook
• S

ta t e (SEQUENCE Non

• Term

ina te

• Ac

t Ca l l e r

• On-Hook
• Ac

t D i sconnec t Ac t ) ) ) ) ) ) ) (DEF INE-PLAN Or i g i n a te

• And
• D

i a l

• P

lan (AND P lan (ALL PLAN-EXPRESSION (SEQUENCE Ca l l e r

• O

f f

• Hook
• Ac

t Connec t

• Dia

l t

• ne
• Ac

t D ia l

• D

ig i t s

• Ac

t ) ) ) ) (DEF INE-CO NCEPT Sys tem-Ac t (AND Ac t i

• n

(ALL ACTOR Sys tem-Agen t ) ) ) (DEF INE-CO NCEPT Connec t

• D

i a l t

• ne
• Ac

t (AND Sys tem-Ac t (ALL PRECONDIT I O N (AND O f f

• Hook
• Sta

te I d l e

• S

ta te ) ) (A l l Add

• L

IST D ia l t

• ne
• S

ta te ) (ALL DELETE-L IST I d l e

• S

ta te (ALL GOAL (AND O f f

• Hook
• Sta

te D ia l t

• ne
• Sta

te ) ) ) ) (DEF INE-CONCEPT Ca l l ee

• O

f f

• Hook
• S

ta te (PR IMIT I VE S ta te ) ) (DEF INE-CONCEPT Ca l l ee

• On-Hook
• S

ta te (PR IMIT I VE S ta te ) ) (DEF INE-CO NCEPT Ca l l ee

• O

f f

• Ca

l l e r

• On
• Sta

te (AND Ca l l ee

• O

f f

• Hook
• S

ta te Ca l l e r

• On
• Hook
• S

tat e ) )

SLIDE 4

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)] -> [(surgery, id1)]

• Plan subsumption

– Action network viewed as bipartite graph matching a4 a2 a5 a5 a1 a1 R P a3 a4 a6 a2 a3 a6 S Q

SLIDE 5

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

Self-organizing method taxonomy

move cargo aircraft

OBJ WITH

move cargo truck

OBJ WITH

move cargo vehicle

OBJ WITH

move cargo C-140

OBJ WITH

Goal:

(move (OBJ ( ins t

• f

ca rgo ) ) (WITH C-140 ) )

Method capability:

(move (OBJ ( ins t

• f

ca rgo) ) (W ITH ( ins t

• f

a i rcra f t ) ) )

SLIDE 6

Reactive Systems

Yolanda Gil CS 541, Fall 2003

(Thanks to Karen Myers from SRI International)

SLIDE 7

Summary

• Control systems

– Networks of “variables” (arcs) and “functions” (nodes)

• Reactive Action Packages (RAPs)

– Networks of “conditions” and “tasks”

• Task Control Architecture (TCA)

– Network arranged according to “vertical capabilities”

• Procedural Reasoning System (PRS)

– Integrates planning, BDI, and reactive techniques

• Anytime algorithms

– When time is short, managing what you think about

• Learning and uncertainty reasoning

SLIDE 8

PRS Interpreter

Execution Cycle

• 1. New information

arrives that updates facts and/or tasks

• 2. Acts are triggered

by new facts or tasks

• 3. A triggered Act is

intended

• 4. An intended Act

is selected

• 5. That intention is

activated

• 6. An action is

performed

• 7. New facts or

tasks are posted

• 8. Intentions are

updated

Goal2 ACT8 sleeping Fact1 ACT2 normal Goal3 ACT3 sleeping

Intention Graph

Cue: (TEST (overpressurized tank.1)) ACT2

Act Library Act Execution

(overpressurized fuel-tank) (ACHIEVE (position ox-valve closed))

New Facts & Tasks External World

1 2 3 4 5 6 7 8

Cue: (ACHIEVE (position valve.1 closed)) ACT1

Facts & Tasks

(ACHIEVE (position ox-valve closed)) ACT1 current

SLIDE 9

Distributed and Multi-Agent Planning

SLIDE 10

Issues

• Who is in charge?
• How distributed?
• How much info is shared?
• Who benefits?
• What and how to communicate?
• How and how much to coordinate?
• Can tasks/goals/resources be negotiated?
• How to handle execution dynamics?

SLIDE 11

Summary (I)

1. Task sharing

– Homogeneous agents – Heterogeneous agents

• Contract nets

– Contactors bid – Managers bid

• Market mechanisms

2. Results sharing

– Blackboard architectures – Distributed constraint satisfaction – Resource sharing through auctions

SLIDE 12

Summary (II)

3. Distributed planning

– Planning approaches

• Cooperative plan Construction
• Centralized planning for Distributed plans
• Distributed planning for Centralized plans
• Distributed planning for Distributed plans

– Execution issues

• Post-planning
• Pre-planning

4. Mental state and collaboration

– Joint intentions – SharedPlans

5. Coordination without communication

SLIDE 13

Mixed-Initiative Planning

SLIDE 14

Challenges

• Interpreting user input

– Mapping into possible operations/responses – Disambiguating requests

• Intelligent search

– Managing classes of solutions – Tracking constraints and previously explored solutions

• Facilitating user’s cognitive task

– Grounding the discussion with a specific plan

• Acting on the user’s input

– Flexible planning framework that can support collaboration

SLIDE 15

Recap and Summary

• Dialogue issues in mixed-initiative planning:

TRAINS

– Interpreting user input, disambiguation – Plan representation as goals/tasks/resources/state + straw plan – Hybrid planning architecture

• Integrating user guidance with a planning

algorithm: PASSAT

– Incorporating the user’s input into a plan generation algorithm