1

Extending the Knowledge Representation Model

Based on

Flexible Inference Engine

By: Shahriar PourazinSupervisor: Dr. Ahmad Abdollahzadeh

AmirKabir University of TechnologyJuly 2005

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Overview

• Project Goals• Introduction• Definitions• Characteristics• Implementation• Achievements• Possible Applications• Conclusion

Overview

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Project Goals

• Design of Flexible Inference Engine– Modifiable Inference (Manual)– Modifying the Inference by Inference (Automatic)– Modifying the Learning by Learning (Recursive Learning)

• Managing the Inference Modules– Modifying the Existing Inferences: New Inference– Organizing the Inference Modules: Runtime Ranking– Selecting the Best or Most Freq. Used Inferences

• Layered Architecture of a Softbot– Just in Time Response (Bounded Resource: Time)– Meta-level Management of Playing Football (Soccer)

Project Goals

4

Making Inference Flexible

?InferenceEngine

KnowledgeBase

Flexible

Introduction

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Upward Reflection for Inference

• An Extremely Simple Knowledgebase K1:K1 = { P1, P2 }P1: (Age Ali 18)P2: (Family Ali Tavana)K1: ((Age Ali 18) (Family Ali Tavana))

• An Extremely Simple Inference F1:F1:F1(K1) = K2 = ((Age Ali 18) (Family Ali Tavana) (Young Ali))R(F1) Ξ (defun F1 (Ki) (push (list Young (second (find-if #'(lambda

(fact) (And (eql `Age (first fact)) (< (third fact) 40)) Ki))) Ki)

• R(F1) is the Reified or Structural Name of F1

• R(F1) is Reflected Up F1

)()40(),(,, xYoungyyxAgeyx

Introduction

6Definitions

Reflecting up and Down for Inference

?InferenceEngine

KnowledgeBase

Flexible

(Family Ali Tavana)

0000 1001 0011 11011100 1000 0010 1111

(defun F1 (p1 p2) (…))

Reification (Reflect UP)Reification (Reflect UP)

Dereification (Reflect Down)Dereification (Reflect Down)

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The Flexible Engine (FlEng)• Traditional Inference:

F1: K1 → K2

• Flexible Inference:F1: K1 U {R(F1)} → K2 U {R(F2 )}F2: K2 U {R(F2)} → K3 U {R(F3 )}F3: …* Requires unique knowledge representation language

• Flexible Inference with Alternate Sets:F1: K1 U {R(F1)} → K2 U {R(F1)} U {R(F2 )}F2: K2 U {R(F1)} U {R(F2)} → K3 U {R(F1)} U {R(F2)} U {R(F3 )} * Requires limiting the number of alternates to be safe

Definitions

{R(F1)} U {R(F2)} U {R(F3)}

• Alternate Set for F:

( (A (3 A1 (p1 p2) (…)) (2 A2 (p1 p2) (…)) (4 A3 (p1 p2) (…)))

(B (1 B1 (q1) (…)) (5 B2 (q1) (…)))

(F (4 F1 (X1 X2) (…)) (7 F2 (X1 X2) (…)) (5 F3 (X1 X2) (…))) )

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Generating Alternatives

( (A (3 A1 (p1 p2) (…))(2 A2 (p1 p2) (…))(4 A3 (p1 p2) (…)))

(B (1 B1 (q1) (…))(5 B2 (q1) (…)))

Consider the following Alternatives:

Given the function C which calls A and B:

(defun C (m n) (+ (A m n) (B m)))

We can Generate Six Alternatives for C:

(w1 C1 (y1 y2) (+ (A1 m n) (B1 m)))(w2 C2 (y1 y2) (+ (A1 m n) (B2 m)))(w3 C3 (y1 y2) (+ (A2 m n) (B1 m)))(w4 C4 (y1 y2) (+ (A2 m n) (B2 m)))(w5 C5 (y1 y2) (+ (A3 m n) (B1 m)))(w6 C6 (y1 y2) (+ (A3 m n) (B2 m)))

Definitions

(C

)

)

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Managing Alternates

• Combinatorial Explosion– Limiting the Number of Alternates– Running Engine in “First Alternate” Mode– Removing the non-Qualified Alternates

• Reserving the Quality of Reasoning (Soundness)– Manual Alternate Generation– Combination of Alternates in Caller Functions– Sharing the Sound Functions Among Agents – Rolling Back the Addition of an Alternate– Using the Qualified Alternates

Characteristics

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Alternate Qualification

• New Alternates Activated after Qualification• We do Post-Qualification (Metareasoning)• Will New Alternate Give the same Output?

– Sorting the Lists– Selecting a Member from a Limited Set– True/False Answers

• Evaluation (Post-Qualification) Function: Voting• Do Existing Alternates Generate Unique Result?

– Navigation– Estimation– Self-Localization

• Post-Qualification Function: Average? History?

Characteristics

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The Test Bed

• Target: Post-Qualification for Self-Localization• A Softbot in Lisp to Play Simulated Soccer• KB and Process both as Lists• Synchronization With Server• Alternate Sets: A List of Lists in Lisp• Pose Estimation: Alternates for Localization• Playing Skill: Alternates of Playing Paths

Implementation

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The S

oftbot Architecture

Alternate Skills

Alternate Pose Calculations

Implementation

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The Timing Problem

Implementation

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The Timing Solution

Implementation

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Meta-level Processes in Softbot

• Managing the Pose Manipulations– Multiple Self-localization Modules– Selecting the Best by Post-Metareasoning

• Generating Alternatives of Functions– Combinatorial Selection of Multiple Alternatives– Special Purpose Alternatives

• Managing Skills– Skills: strings of snapshots in actions– Choosing the Skills– Switching in between Skills

Implementation

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Pose Estimation

• Five Self-Localization Algorithms

• Not Always Possible to Predict the Best

• None of Them Too bad to be Ignored

• Failed/Inexact Outputs Found at the End

• Different Answers Most of the Time

• Post-Qualification of Answers Possible

Implementation

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The Selection Algorithm

Implementation

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Selecting the Best Among the Results

Implementation

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The Accumulation of Error

Implementation

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Averages, History, and Best

Implementation

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Achievements• Design of Flexible Inference Engine

– Modifiable Inference (Manual)– Modifying the Inference by Inference (Automatic)– Modifying the Evaluation by Evaluation (Meta-Evaluation)

• Alternate Sets of Inference Modules– New Alternate Inference: Modified Existing Inference– Weighted Alternate Inference Modules– Weights as the Best or Most Freq. Used Inferences– Runtime Inference Module Ranking/Weighting– Selecting Alternates Before/During/After Running

• Layered Architecture of a Softbot– Just in Time Response to Environment (Bounded Resource: Time)– Alternate Skills of Playing Football (Soccer)– Alternate Pose Estimation Modules– Choosing the Best Pose Estimation Alternate by Post-Metareasoning

Achievements

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Possible Applications

• Automatic Debugging in Software Engineering• Creativity in Design by Modifiable Alternates• Alternate Embedding and Random Call for Games• Partial Alternate Settling to Locate Insanity• Opponent Modeling with Faulty Alternates• Static, Dynamic and Now Revocable Libraries • Processors Calling Multiple Functions at Once• User Modeling by Group Empty Alternate Templates• Safe Random Reactions in Commanding War

Possible Applications

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Conclusion

• Safe Metareasoning by Alternate Sets• Classes: Pre/Para/Post Metareasoning• Parallel Metareasoning• The Followings are Now Possible:

– Late Debugging by Metareasoning– Alternate Functions to deal with Alternates– Global Libraries of Alternate Processors– Agents that Share Inference Mechanisms– Group Function Call in Parallel Processing– Web Services Approach to Reasoning– Keeping Away From the Blind Program Modification– Dynamic Libraries which are Replaced at Runtime

Conclusion

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Publications• Barforoush A. A., Pourazin S., Shamsfard M., “A Design for Knowledge Partitioning: Introducing an

Intermediate Reflective Model”, Proceedings of the 4th Annual International CSI Computer Conference (CSICC’98), Sharif University of Technology, Tehran, pp. 100–109, 1999. 

• Barforoush A. A., Shamsfard M., Pourazin S., The Relation of Knowledge, Inference and Representation Models, Amir-Kabir Journal of Science and Technology, Vol. 12, No. 46, pp. 177-187: Amir-Kabir University Press, Tehran, Iran, Spring 2001.

• Shahriar Pourazin, et al, “Pardis, Our team in RoboCup Simulation League”, Team Descriptions Simulation League, Linkoping University Electronic Press, pp. 95-97, 1999  (PDF)

• Shahriar Pourazin: Pardis. 614-617 in Manuela M. Veloso, Enrico Pagello, Hiroaki Kitano (Eds.): RoboCup-99: Robot Soccer World Cup III. Lecture Notes in Computer Science 1856 Springer 2000, ISBN 3-540-41043-0 (URL: http://www.informatik.uni-trier.de/~ley/db/conf/robocup/robocup1999.html#Pourazin99) (PDF)

• Shahriar Pourazin: Simulated Soccer Robot: Toward Being on Time; Proceedings of Eighteenth IASTED International Conference: Applied Informatics,  February 14-17, 2000, in Insbruck, Austria. Editor: M.H. Hamza; 2000, 877 pages, ISBN: 0-88986-280-X (307); ISSN: 1027-2666 (PDF)

• Shahriar Pourazin, Ahmad Abdollah Zadeh Barforoush: FlEng: The Flexible Inference Engine; Proceedings of Eighteenth IASTED International Conference: Applied Informatics,  February 14-17, 2000, in Insbruck, Austria. Editor: M.H. Hamza; 2000, 877 pages, ISBN: 0-88986-280-X (307); ISSN: 1027-2666 (PDF)

• Shahriar Pourazin: A Portable Architecture for RoboCup Agents; Fourth International Conference on AUTONOMOUS AGENTS (Agents 2000) Barcelona, Catalonia, Spain. June 3 - June 7 (PDF)

• Shahriar Pourazin, Ahmad Abdollahzadeh Barforoush: Concurrent Metareasoning; Journal of Supercomputing, Kluwer; Accepted. (PDF)

• Shahriar Pourazin, Ahmad Abdollahzadeh Barforoush: Going Meta: Back to the Expectations; CSI Journal of Computer Science and Engineering, Accepted subject to changes. (PDF)

Publications

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Questions

?

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Submitted Papers• Shahriar Pourazin, Ahmad Abdollahzadeh Barforoush: Learning FlEng:

The Implementation Notes; Journal of Artificial Intelligence, Elsevier Science,

– Submission Date: July 13 2002. (PDF)• Shahriar Pourazin, Ahmad Abdollahzadeh Barforoush: The Soccer Softbot

in Lisp; International Journal on Artificial Intelligence tools, World Scientific Publishing Company,

– Submission Date: February, 28, 2003. (PDF)• Shahriar Pourazin, Ahmad Abdollahzadeh Barforoush: Dynamic Selection

of Algorithms by Flexible Inference; Amir-Kabir Journal, Amir-Kabir University of Technology,

– Submission Date: March 12, 2003.  (PDF)• Shahriar Pourazin, Ahmad Abdollahzadeh Barforoush: MetaReasoning in

Artificial Intelligent Systems; Computer Society of Iran Journal; – Submission Date: January 22, 2005. (PDF)

• Shahriar Pourazin, Ahmad Abdollahzadeh Barforoush: Classifying the Types of Metareasoning; Mashad Ferdowsi University Journal,

– Submission Date: January 21, 2005. (PDF)

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Arrangement: The Snapshot

( WF <Simulation-Cycle> ( B <X-ball> (Y-Ball> <Direction> <Speed> ) ( <Team-of-us> <Score> ( 1 <X> <Y> <Body-dir> <Velocity> <Neck-dir> <Stamina> ) ( 2 <X> <Y> <Body-dir> <Velocity> <Neck-dir> <Stamina> ) ( 3 <X> <Y> <Body-dir> <Velocity> <Neck-dir> <Stamina> ) ( 4 <X> <Y> <Body-dir> <Velocity> <Neck-dir> <Stamina> ) <Player5> ... <Payer11> ) ( <Team-of-Them> <Score> <Player1> <Player2> ... <Payer11> ) )

Implementation

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Playing S

kills: S

tring of Snapshots

(skills 1.0 (tactic1 ( wf 1 (b 0 0) (myteam nil (P1 (gt -2) 0) ) (opponent nil ) ) ( wf 2 (b 0 0) (myteam nil (P1 0 0) ) (opponent nil ) ) ( wf 3 (b 3 0) (myteam nil (P1 3 0) ) (opponent nil ) ) ( wf 4 (b 7 0) (myteam nil (P1 7 0 0) ) (opponent nil ) ) ( wf 5 (b xb yb) (myteam nil (P1 xb yb 0) ) (opponent nil ) ) ( wf 6 (b (+ xb 3) yb) (myteam nil (P1 (+ xb 3) yb 0) ) (opponent nil ) ) ) )

Implementation