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Adaptive Loose Coupling Intelligent Rule System ALCIRS Research Skills The Presentation Presented by: Irfan Subakti 1054257 Supervisor: Prof. John A. Barnden School of Computer Science University of Birmingham United Kingdom 25 January

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SLIDE 1

Research Skills – The Presentation

Presented by: Irfan Subakti – 1054257 Supervisor: Prof. John A. Barnden School of Computer Science University of Birmingham United Kingdom 25 January 2012

Adaptive Loose Coupling Intelligent Rule System ALCIRS

25 Jan 2012 1 Research Skills | ALCIRS | Irfan Subakti (1054257)

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SLIDE 2

Ideas

25 Jan 2012 Research Skills | ALCIRS | Irfan Subakti (1054257) 2

 Adaptive

 Able to adjust to another type of situation

 Loose coupling

 Overcome rule dependency changing problem

 Intelligent

 Learn for improving its rule semantic understanding,

rule learning & generating

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SLIDE 3

Motivation

25 Jan 2012 Research Skills | ALCIRS | Irfan Subakti (1054257) 3

 Variable-Centered Intelligent Rule System (VCIRS) - Subakti

(2005, 2006, 2007)

 Monotonically increasing Rule Based (RB) as time goes by  Tight coupling  inflexibility in rule changing  Too simple rule generating  need more creativity

 Blackboard Systems – Erman et al. (1980), Corkill (1991)

 Dealing with complex applications which are roughly defined 

flexible in representation & in contributing problem solving

 Disadvantage: formal specification

 Contextual Ontologies – Benslimane et al. (2006)

 A concept’s set of properties is vary depend on a context

 Semantic Understanding – Shih et al. (2011)

 Capturing the interpretation of the behaviours and situations

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SLIDE 4

Motivation (cont’d)

25 Jan 2012 Research Skills | ALCIRS | Irfan Subakti (1054257) 4

 Robust Growing Neural Gas (RGNG) – Kin and Suganthan

(2004)

 Robust properties in clustering  Outlier resistance  Adaptive modulation of learning rates  Cluster repulsion  Insensitivity  Initialization  Input sequence ordering  Outlier presence

 Particle Swarm Optimization (PSO) - Kennedy and Eberhart

(2001)

 Simple idea with outstanding result in optimisation

 Creativity in Reasoning – Indurkhya (1997)

 New categories & interpretation can be created in legal reasoning

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SLIDE 5

Basic Component

 Blackboard model

(Corkill, 1991)

 ALCIRS

5 Research Skills | ALCIRS | Irfan Subakti (1054257) 25 Jan 2012

Blackboard Knowledge Sources Control Component

Inference Engine Rule Based Rules Interface Implement Ontology Contextual Ontology

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SLIDE 6

Basic Framework

 PSO (Kennedy and Eberhart,

2001 )

 RGNG (Kin and Suganthan,

2004)

 Rule generating  creativity

(Indurkhya, 1997)

6 Research Skills | ALCIRS | Irfan Subakti (1054257) 25 Jan 2012

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SLIDE 7

Basic Framework (cont’ed)

 Basic BS

(Corkill, 1991)

 Basic ALCIRS

7 Research Skills | ALCIRS | Irfan Subakti (1054257) 25 Jan 2012

Blackboard Executing KS Activation Pending KS Activations Library of KSs Control Components Events

Inference Engine PSORGNG - clustering Adaptive Rule dependency Loose Coupling Rule learning, reasoning, generating Intelligence Rule Based Rules Interface Implement Ontology Contextual Ontology

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Adaptive - Methodology

25 Jan 2012 Research Skills | ALCIRS | Irfan Subakti (1054257) 8

 Adaptive

 Raw data

 Rules will be clustered in proper place, using PSORGNG

 Existing rules in Rule Base (RB)

 Interface & Implement parts will be classified, supported by

Contextual Ontology  Generating new rules

 Supported by Contextual Ontology

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SLIDE 9

Loose Coupling - Methodology

25 Jan 2012 Research Skills | ALCIRS | Irfan Subakti (1054257) 9

 Loose coupling

 Rule dependency changing

 Each rule has

 Interface

  • A part that can be shared to other rules  global
  • Other rules may use a little or none of this part
  • Flexibility concept applied, since all rules loosely can be

connected with this part

  • As a bridge for contextual ontologies layer

 Implement

  • a specific part which dedicated to its rule  local

 Ontology

  • Linked to contextual ontology  further rule learning, reasoning

& generating

 Core ontology  the lowest level of contextual ontology can be used

as the last resort if higher contextual ontologies failed to do so

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Intelligence - Methodology

25 Jan 2012 Research Skills | ALCIRS | Irfan Subakti (1054257) 10

 Intelligence

 Semantic understanding

 Understand the meaning of rule given a context 

supported by contextual ontology  Rule learning, reasoning & generating

 Contextual ontology  continually learning to optimise the

usefulness of the rules

 Contextual reasoning  supported by contextual ontology

gives an inference based on the context

 Core ontology  performing creativity in producing a new

rule from the existing rules in RB given a new case

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Loose Coupling

 Looseness definition

 When a rule only uses none or little part of other

rules  loose coupling mechanism  Part usages on rules

 None

 Explicit: direct assignment. E.g., weight =

input_weight

 Implicit: by using the contextual ontology

 Little part

 Using exactly the same term. E.g., Rule #1 uses

input_weight in its Interface, while Rule #2 also uses input_weight in its Implement

11 Research Skills | ALCIRS | Irfan Subakti (1054257) 25 Jan 2012

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SLIDE 12

Case Study (1)

 Supermarket goods purchasing

12 Research Skills | ALCIRS | Irfan Subakti (1054257) 25 Jan 2012

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Case Study (2)

 Owning a car and a house

 An example of a rule, which has

 Interface  Implement  Ontology

13 Research Skills | ALCIRS | Irfan Subakti (1054257) 25 Jan 2012

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Case Study (3)

 Owning a car and a house

(continued)

 Loose coupling rules example

#1

 A user starts creating a new

rule

#Car-house owning#  defining a relation between owning a car and a house

#Vehicle type#  defining the types

  • f vehicles

 Loose coupling

No part from #Car-house owning# is used in #Vehicle type#

Direct assignment: weight = input_weight at #Vehicle type#

14 Research Skills | ALCIRS | Irfan Subakti (1054257) 25 Jan 2012

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Case Study (4)

 Owning a car and a house

(continued)

 Loose coupling rules example

#2

 Another day, the user willing to

add up a rule

 #House type#  defining the

types of houses

 Loose coupling

 Little part from #Car-house

  • wning# is used in #House

type#

house_type is used in both rules  Little part from #Vehicle type# is

used in #House type#

vehicle_type is used in both rules 15 Research Skills | ALCIRS | Irfan Subakti (1054257) 25 Jan 2012

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Case Study (5)

 Owning a car and a house

(continued)

 Loose coupling rules example

#3

 Then it turned another day and

the user willing to add up a rule

#Garage type#

 Loose coupling

Little part from #Vehicle type# is used in #Garage type#

wheels is used in both rules 

No part from #Car-house owning# is used in #Garage type#

Direct assignment: wheels = input_wheels at #Vehicle type#

16 Research Skills | ALCIRS | Irfan Subakti (1054257) 25 Jan 2012

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Case Study (6)

 Owning a car and a house

(continued)

 A whole rules in RB

 Loose coupling

No part from #Car-house owning# is used in #Vehicle type#

Direct assignment: weight = input_weight at #Vehicle type#

Little part from #Car-house owning# is used in #House type#

house_type is used in both rules

Little part from #Vehicle type# is used in #House type#

vehicle_type is used in both rules

Little part from #Vehicle type# is used in #Garage type#

wheels is used in both rules

No part from #Car-house owning# is used in #Garage type#

Direct assignment: wheels = input_wheels at #Vehicle type#

17 Research Skills | ALCIRS | Irfan Subakti (1054257) 25 Jan 2012

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Rule Generating

25 Jan 2012 Research Skills | ALCIRS | Irfan Subakti (1054257) 18

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Anticipated Result

 Adaptive

 Rule is clustered in certain group  establishing a rule

for each formed cluster

 Interface & Implement is classified in certain category  New rule can be generated for a new case  creativity

 Loose coupling

 Rule changing/updating in a given contextual meaning is

easily performed without worry about rule dependency  Intelligent

 Comprehend the meaning of rule given a context  Optimise the usefulness of the rules  Contextual reasoning  Able to perform creativity in a new rule creation

19 Research Skills | ALCIRS | Irfan Subakti (1054257) 25 Jan 2012

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Conclusion

 Adaptive Loose Coupling Intelligent Rule System

(ALCIRS)

 A Rule-Based system  Use a specific framework which works adaptively &

intelligently comparing to Blackboard Systems

 Treating rule in the loose coupling manner

 Rule dependency in a given context is automatically preserved

 Rule updating is easily perform without worry about this

dependency

 Rules are clustered and classified automatically

 Understanding the contextual meaning of given rule  Optimising usefulness of the rules  Contextual reasoning

 Perform creativity in the rule generation

20 Research Skills | ALCIRS | Irfan Subakti (1054257) 25 Jan 2012

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Future Work

 Continue reading the literature and

comprehend it deeper to suit the proposed system

 Implementing the framework

 Using some examples

21 Research Skills | ALCIRS | Irfan Subakti (1054257) 25 Jan 2012

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Thank you for your attention! 

Adaptive Loose Coupling Intelligent Rule System ALCIRS

25 Jan 2012 22 Research Skills | ALCIRS | Irfan Subakti (1054257)

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References

1.

Benslimane, D, Arara, A., Falquet, G, Maamar, Z., Thiran, P. and Gargouri, F. (2006) Contextual Ontologies - Motivations, Challenges, and Solutions. In: Proceedings

  • f the Fourth Biennial International Conference on Advances in Information Systems (ADVIS 2006), 18-20 October 2006. Izmir-Turkey. Advances in Information

Systems Lecture Notes in Computer Science (2006), 4243. Springer, pp. 168-176.

2.

Corkill, D.D. (1991) Blackboard Systems. AI Expert, 6 (9), pp. 40-47.

3.

Erman, L.D., Hayes-Roth, F., Lesser, V.R. and Reddy, D.R. (1980) The Hearsay-II Speech-Understanding System: Integrating Knowledge to Resolve Uncertainty. Computing Surveys, 12 (2), pp. 213-253.

4.

Indurkhya, B. (1997) On Modeling Creativity in Legal Reasoning. In: Proceedings of the 6th International Conference on Artificial Intelligence and Law (ICAIL-97), Melbourne, 30 June-3 July 1997. New York: ACM, pp. 180-189.

5.

Kennedy, J. and Eberhart, R.C. (2001) Swarm Intelligence. San Francisco: Morgan Kaufmann.

6.

Kin, A.K. and Suganthan, P.N. (2004) Robust Growing Neural Gas Algorithm with Application in Cluster Analysis. Neural Networks, 17, pp. 1135-1148.

7.

Shih, F., Narayanan, V. and Kuhn, L. (2011) Enabling Semantic Understanding of Situations from Contextual Data in a Privacy-Sensitive Manner. In: Proceedings of the Workshops at the Twenty-Fifth AAAI Conference on Artificial Intelligence (AAAI-11), 7-8 August 2011. San Francisco -USA. AAAI Press, pp. 68-72.

8.

Subakti, I. (2005) A Variable-Centered Intelligent Rule System. In: Proceedings of the 1st Annual International Conference: Information and Communication Technology Seminar (ICTS2005), 1 (1), Surabaya-Indonesia, 11 August 2005. Surabaya-Indonesia: Sepuluh Nopember Institute of Technology (ITS), pp. 167-174.

9.

Subakti, I. (2006) Some Revisions in VCIRS and Cases Reconstructing Perspectives. In: Proceedings of the 2nd Annual International Conference: Information and Communication Technology Seminar (ICTS2006), 1 (1), Surabaya-Indonesia, 29 August 2006. Surabaya-Indonesia: Sepuluh Nopember Institute of Technology (ITS), Surabaya-Indonesia, pp. 233-238.

  • 10. Subakti, I. and Wijayanto, O. (2006) Penerapan Konsep Fuzzy dalam Variable-Centered Intelligent Rule System (Studi Kasus: Pemilihan Jurusan di Chinese

University of Hongkong) (The Implementation of Fuzzy Concepts in Variable-Centered Intelligent Rule System (Case Study: Department Admission in Chinese University of Hongkong)). Jurnal Informatika, 7 (2), November 2006. Surabaya-Indonesia: The Institute of Research & Community Outreach - Petra Christian University, pp. 98-107.

  • 11. Subakti, I. and Hidayatullah, R. (2007) Aplikasi Sistem Pakar Untuk Diagnosis Awal Gangguan Kesehatan Secara Mandiri Menggunakan Variable-Centered Intelligent

Rule System (An Application of Expert System for Independent Healthy Problem Pre-diagnosis Using Variable-Centered Intelligent Rule System). Jurnal Ilmiah Teknologi Informasi (JUTI - Scientific Journal of Information Technology), 6 (1), January 2007, ISSN: 1412-6389. Surabaya-Indonesia: Sepuluh Nopember Institute of Technology (ITS), pp. 11-16.

  • 12. Subakti, I. and Wijaya, A.B.A. (2006) Pembuatan Role-Playing Game Berbasis Web Menggunakan Variable-Centered Intelligent Rule System (A Role Playing Game

Web-Based Building Using Variable-Centered Intelligent Rule System). Undergraduate Thesis, Department of Informatics, Faculty of Information Technology, Sepuluh Nopember Institute of Technology (ITS), Surabaya-Indonesia.

23 Research Skills | ALCIRS | Irfan Subakti (1054257) 25 Jan 2012