sub-project iii 4 : cresco-soc-cog second progress report ( 15 february 2007 - 14 june 2007)

Sub-Project III 4:

CRESCO-SOC-COG Second Progress Report

(15 February 2007 - 14 June 2007)

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-Massimiliano Caramia

Coordinator : Adam Maria Gadomski, ENEA

Second cresco coordination meeting, Roma, 6 July 2007

Contribution of the Dipartimento di Ingegneria dell’Impresa

Università di Roma “Tor Vergata”

Information, Preferences and Knowledge (IPK model)- An Information is data describing a state (or a property) of an object or entity of interest.

- knowledge is every abstract property of a human or artificial agent which has the ability to process an information into another information.

- A preference is an ordered relation between two states (properties) of a domain of the activity of an agent. It indicates a property with higher utility for an agent.

Preference relations serve to establish an intervention goal of an agent.

Information, preferences and knowledege are essential components for every decision process.

DII - Università di Roma “Tor Vergata” Contribution

Introduction: Conceptualization Platform

The general framework

• Given a state (information) X of the system, a knowledge Kj transforms X into another state (information) Y.

• An intelligent agent (IA) has a set of knowledge K = (K1,..,Kn) to exploit.

• IA wants to choose the knowledge Kj* in K that allows the current system state X to be trasformed into the requested state (goal)

• Preferences allow the IA to compare the possible (expert based) outcomes of different knowledge and make a decision


How it works– Information I arrives from a domain of activities– It is transformed by the set of (model-) knowledge K

producing a set of new information– Information are confronted with preferences to

establish the goal, i.e., a state maximally preferred– Goal enables to choose the appropriate (operational-)

knowledge Kj* in K– Kj* processes information: I'= Kj*(I)– The new information indicates how to modify the

domain of activity


The IPK decision network

Different points of view. …. Different meta-levels…

Management is focused on first meta-level.

Managerial decisions level


Universal Management Paradigm

Subjective socio-cognitive perspective: … Relation to the large organization structure …..


I,P,K I,P,K I,P,K I,P,K

Supervisor Supervisor Supervisor

Manager Manager Manager Manager

Tasks, information

A distributed modeling framework of IPK and UMP

Tasks, information

Tasks, information

Tasks, information

Tasks, information

Tasks, information


Top-view

The model proposal

• Implementing a distributed IPK and UMP model in a grid infrastructure

• An study example: Applying a market based model (intervention domain) to let actors/agents negotiate for the achievement of their intervention-task (from a supervisor)

• Experimentation on a set of verification & validation instances (syntetic)

• Application to the selected real test cases of the socio-technological network under high-risk decisions.


1. Organization modeled as a computer network

2. How to mitigate organization vulnerability.

3. The first aspect refers to the the information exchange, communication and tasks distribution

4. The messages are carriers of IPK

5. What is managed by a manager?


The Grid framework (Ranganathan and Foster, 2002)

Doamain of

activities

Supervisors

Manager

Economic Models for Grid Resource Management• Provide a quantitative framework for resource allocation and

for regulating supply and demand in the Grid computing environments

• They primarily charge the end users for services that they consume on a demand-and-supply basis

• They optimize resource provider and consumer objective functions through trading and brokering services

• A user is in competition with other users and a resource owner with other resource owners


In economy, high-risk decisional example:

The example study: Economic Models• Commodity Market Model

• Posted Price Model

• Resource Sharing Model via Negotiation

• Tendering/Contract-Net Model

• Auction Model

• Bargaining model

• Bid-based Proportional Resource Sharing Model

• Community/Coalition/Bartering Model

• Monopoly and Oligopoly


All of them are based on the Domain-of-Activity attributes (for identification) and attributes of Intelligent Agent

Main Players in the Grid Market Place

• Grid Service Providers (GSPs) providing the role of producers. In the TOGA context they are managers, informatives, and advisors

• Grid Resource Brokers (GRBs) representing consumers. In the TOGA context they are supervisors

• Grid Market Directory (GMD) to mediate the interaction between GRBs and GSPs. In the TOGA context they represent meta-knowledge and meta-information


Two decisional perspectives (different top-tasks): Tender-Contract Net Model

From the resource broker perspective:• The broker announces its requirements and invites bids from GSPs.• Interested GSPs evaluate the announcement and respond by submitting their bids.• The broker chooses the best offer and sign a contract to the most appropriate GSP.

From the GSP perspective:• It receives announcements.• It evaluates the service capability.• It responds with a bid.• It delivers service if bid is accepted.• It reports results and bill the broker.

Buyya et al., 2002


The Simulation StudyTwo different scenarios for the Grid system:

1. Scenario ECO1: tasks are mono-thematic applications and their requests are submitted to the same ES (GRB) that interacts with the LSs (GSPs) of clusters dedicated to that kind of applications.

2. Scenario ECO2: tasks are heterogeneous and there are as many GRBs as many tasks.

• In Scenario ECO2 the GSP of a cluster may receive awards from many GRBs, and it will respond with an acceptance only to the award related to the most useful announcement for the cluster, and with a refusal to the other awards.


The possibilities of data processing: The Data Set

• Tasks arrive according to a Poisson arrival process, where is the average # of tasks per t.u.

• 45% of arriving tasks are background tasks, and they have priority with respect to external tasks.

• Average task size Oj = 10000 MI

• 1000 t.u.

• Average task budget Bj = 250 G$

Grid1: |M| = 10 identical clusters, with 10 machines eachGrid2: |M| = 11 clusters with different number of machines

according to WWG Testbed, Buyya et al. (2002).



- We compared ECO1 and ECO2 with Round Robin protocol

- We analysed: load goal function, utility goal function, penalty goal function

- Load and utility are the two main goals in the system: the former refers to the manager, the latter to the supervisor

Average Load (Grid 1)

0102030405060708090

100

1 2 3 4 5 6 7 8 9 10

bkg load

RR

ECO1

ECO2

Average penalty of processed tasks (Grid 1)

0

50

100

150

200

250

1 2 3 4 5 6 7 8 9 10

RR

ECO1

ECO2

Average utility of processed tasks (Grid 1)

0

20

40

60

80

100

120

140

1 2 3 4 5 6 7 8 9 10

RR

ECO1

ECO2

Average Load (Grid 2)

0102030405060708090

100

1 2 3 4 5 6 7 8 9 10

bkg load RRECO1 ECO2

Average penalty of processed tasks (Grid 2)

0

40

80

120

160

200

240

1 2 3 4 5 6 7 8 9 10

RR

ECO1

ECO2

Average utility of processed tasks (Grid 2)

020406080

100120140160

1 2 3 4 5 6 7 8 9 10

RR

ECO1

ECO2

Case study: future work

- State of the art analysis

- Distributed model proposal

- Mapping between TOGA and Computer network

- Implementation and testing of the model

- Preliminary results on validation instances

- Future work: tests on case study

Conclusions:

Some referencesGadomski A.M. (1994), TOGA: A Methodological and Conceptual Pattern for Modeling of Abstract Intelligent Agent. In Proceedings of the "First International Round-Table on Abstract Intelligent Agent". A.M. Gadomski (editor), 25-27 Gen., Rome, 1993, Publisher ENEA, Feb.1994Gadomski A. M., S. Bologna, G.Di Costanzo, A.Perini, M. Schaerf. (2001), “Towards Intelligent Decision Support Systems for Emergency Managers: The IDA Approach”. International Journal of Risk Assessment and Management.Gadomski A. M., (2003), Socio-Cognitive Engineering Foundations and Applications: From Humans to Nations, Preprints of SCEF2003 ( First International Workshop on Socio-Cognitive Engineering Foundations and Third Abstract Intelligent Agent International Round-Tables Initiative), Rome, 30 Sep. 2003.Gadomski A.M. , A. Salvatore, A. Di Giulio (2003) Case Study Analysis of Disturbs in Spatial Cognition: Unified TOGA Approach, 2nd International Conference on Spatial Cognition, RomeGadomski A.M. (2006), Socio-Cognitive Scenarios for Business Intelligence Reinforcement: TOGA Approach, The paper preliminary accepted for publication in Cognitive Processing, International Quarterly of Cognitive Science, Springer Verlag.

sub-project iii 4 : cresco-soc-cog second progress report ( 15 february 2007 - 14 june 2007)

Documents

roma tor vergatainformation

knowledge ipk

set of knowledge

state information x

state information y

informationdii universit

decisiondii universit

set of model