distributed models for decision support jose cuena & sascha ossowski pesented by: gal moshitch...

Distributed Models for Decision Support

Jose Cuena &

Sascha Ossowski

Pesented by:

Gal Moshitch &

Rica Gonen

Motivation

The outside world is full of systems which are governed by complex laws of behavior.

Those systems can be:– Unanimated entities governed by laws of physics– Organizations of humans with artificial process

rules.

Often there is a need to influence their dynamics into a desired direction.

Motivation

For example:– Computer networks - managed in order to maintain

upper bound on message delays.

– Road traffic flows - influenced to avoid traffic jams.

– Air traffic control – influence the planes’ routes to avoid accidents.

The goal:– Maximize efficiency.– Minimize negative impact of faults.

Motivation

Increasing data volume Computer applications

Decreasing time horizon support the responsible person

= Decision support systems (DSS)

Outline

We will discuss:1. Construction principles of DSS.

2. Distributed AI (DAI) models and architectures that applied in DSS.

3. Applications for energy management and traffic management.

Construction Principles of DSS.

Modeling DSS:– A set of world states S

given by values of the state and control variables.

– ideal states,

undesired states,

State values and control variables that should be

achieved or avoided.

S

S

,S S S


Modeling DSS: (cont.)– A notion of preference on states

“How close” one state is to another - Partial order/Metric

– A set of control actionsControl variables are changed directlyState variables are modified indirectly during the

system evolution


Crucial questions DSS should know the answer on:– What is happening?

“understand” a situation by identifying advantageous and problematic aspects.

– What may happen?The evolution of the system if no intervention

takes place. – What should be done?

Which are the most convenient actions improve the results.

Knowledge-Based DSS

Knowledge-Based DSS apply - divide and conquer strategy.

An example of a task-methods-subtasks tree (TMST).

Knowledge-Based DSS

Task-oriented modeling:– The classification task

classifies the situation with respect to its desirability. Output set of problematic features of the current situation.

– The diagnosis task An explanation that identifies the causes of such

undesirable behavior.

– The prediction task Evaluates how state S will evolve into state S’ given certain

values for the control variables.

Knowledge-Based DSS

Task-oriented modeling:(cont.)– The option generation task

Generates a set of plans to overcome the problems identified.

– The action selection task Selects which of the potential plans will be the outcome of

the management process.

Distributed AI (DAI) Models

Agent-based structuring introduces a more complex notion of modularity to computer science.

Notion of agents allows:– Level of specialty

Designing agents that specialized in basic functions

– Level of autonomy Integrate in an agent a set of functions required for the

whole application but limited in scope. (i.e. time, space).

Generality of agent allows:– Human principles for structuring organizations as design

criteria.


The coordination problem has two solutions approach: Centralized

– Special coordinator agent responsible for detecting interdependencies between the local agents’ activities.

Decentralized – No such special agent exists– Agents interact laterally– They have the knowledge to discover inconsistencies

between their intended actions and mutually adapt their local decisions.


Centralized approach:– All possible cases of inconsistencies analyzed a

priori and taken into account by upper level modules.

– DisadvantageIf additional lower level models are introduced, a

sequence of changes has to be produced in the upper level models.


Decentralized approach:– Advantages:

Systems that are easier to build

(defined very accurately only at the local level) Easy maintenance Stable coexistence independent of the number of agents in

society. No problems of propagation to upper levels appear.

– Disadvantage: Quality of the intelligence of the whole society of agents.

Distributed AI (DAI) Architectures for DSS.

The architecture does not consider computation and efficiency.

It considers only features necessary for different case studies.


The architecture is built around three major components:– A perception subsystem

Allows the agent to be situated in the environment and in society by perceiving agent messages.

– An intelligence subsystem Manages the different aspects of information processing as

well as individual and social problem-solving.

– An action subsystem Enacts the plans produced by the intelligent subsystem Displaying messages to the control personal Sending messages to other agents


The architecture is composed of three models:– Information Model– Knowledge Model– Control Model

Information model and knowledge model focuses on the intelligence subsystem

Control model focuses on the action subsystem.

Information Model

The agents’ dynamic beliefs about the world itself and the others are stored in the information model.

The perception subsystem writes data on the information model.

When the intelligence subsystem’s knowledge is enacted, the information model is modified.

The action subsystem reads from the information model.

Information Model

The information model composed of two types of information:– Problem-solving information

Local problem-solving tasks informationSocial problem-solving task information

– Control informationAn agenda of what is “intended to be done”

– Task agenda – keeps track of the tasks to be achieved locally.

– Conversation agenda – keeps track of the social methods in which it is involved.

Knowledge Model

Agent knowledge can be classified from two perspectives.– Problem solving knowledge

which actions to take

– Strategic knowledge helps to choose among different options that the intelligence

subsystem is to process next.

Agent knowledge can be classified according to its role – Individual agent knowledge– Social knowledge

Knowledge Model

Individual agent knowledge:

– Motivation knowledge A collection of patterns modeling different classes of events

considered by the agent as relevant in the external world.

– Local problem-solving knowledge Basic methods

– perform elementary functions by specific algorithms or constraints.

Compound methods– TMST tree, rules or hard-coded simple algorithm.

Knowledge Model

Individual agent knowledge (cont.):

– Local strategic knowledge Generation of the TMST tree.At every level and for every task selects the

method to be used.

Knowledge Model

Social knowledge:– Acquaintance models

Knowledge about other agents is stored in these models. By application of a pattern matching method it can be

deduced whether and up to which degree some acquaintance provides desired characteristics.

– Social strategic knowledge Determines the next conversation to work on. Generation of the TMST tree when methods of different

agents integrated.

Knowledge Model

Social knowledge (cont.):– Social methods:

Copes with a task by solving its subtasksSpecify at a very high level how these subtasks

are to be integrated.Task assignment

– Selection of an agent, when several available.

Knowledge Model

Social knowledge (cont.):– Social methods (cont.):

Task synchronization– Once tasks are assigned, the flow of information

between them needs to be synchronized.

Solution integration– The results of subtasks of a social method need to be

adapt to each other in order to receive a consisting result.

Control Model

Perception Subsystem

Intelligence Subsystem

Action Subsystem

Messages

Perceptions

Messages

Actions

Motivation Knowledge

Local Strategic Knowledge

Acquaintance Models

Social Strategic Knowledge

Local Problem Information

Social Problem Solving

Conversation Agenda

Local Problem Solving

Knowledge

Social Methods

Strategic Knowledge

Information Model

Problem Solving Knowledge

Local Social

Problem Solving

Inf

Control Inf

Task Agenda

Control Model



Action Subsystem

Messages

Perceptions

Messages

Actions



Acquaintance Models




Conversation Agenda


Knowledge

Social Methods

Strategic Knowledge

Information Model


Local Social

Problem Solving

Inf

Control Inf

Task Agenda (Add)

Control Model



Action Subsystem

Messages

Perceptions

Messages

Actions



Acquaintance Models




Task Agenda (Add)

Conversation Agenda


Knowledge

Social Methods

Strategic Knowledge

Information Model


Local Social

Problem Solving

Inf

Control Inf

Control Model



Action Subsystem

Messages

Perceptions

Messages

Actions



Acquaintance Models




Task Agenda (Execute Sum)

Conversation Agenda


Knowledge

Social Methods

Strategic Knowledge

Information Model


Local Social

Problem Solving

Inf

Control Inf

Control Model



Action Subsystem

Messages

Perceptions

Messages

Actions



Acquaintance Models




Conversation Agenda


Knowledge

Social Methods

Strategic Knowledge

Information Model


Local Social

Problem Solving

Inf

Control Inf

Task Agenda

distributed models for decision support jose cuena & sascha ossowski pesented by: gal moshitch...

Documents

modeling dss

state values

state s

crucial questions dss

traffic management

set of plans

air traffic control

set of world states