final presentation

DOAS FINAL PRESENTATION

Interactive Response System for Crisis Management

4-2-2005

Gijs Dubbelman [[email protected]]Ivo Everts [[email protected]]

Tom van der Weide [[email protected]]Siwei Wang [[email protected]]

Introduction

• Police, firesquad and medics need up to date information during a crisis (via a control room).

• Sensors alone may not fulfill this need, and/or they may be malfunctioning.

• People near the crisis area can provide valuable information.

Our task

• Design and implement a system that can determine whether or not more information is needed about a crisis situation. If so, contact to humans in the crisis area in order to retrieve the desired information.

Distributed Perception Networks

• We use a DPN.

• Recap: A DPN is a multi-agent based approach to fusion of heterogeneous and distributed data. Bayesian inferences can be made. Data fusion node: Fusion Agent, sensor interpreting node: Sensor Agent.

• Actually, we use JavaBayes, which is also being used by the UvA DPN sofware.

Basic system flow (1)

• A sensor registers a sudden change in value on a certain location.

• All Sensor- and Fusion- Agents that are affected by this sensor are investigated, by the Concept Manager Agent.

• Those Fusion Agents with high uncertainty in their hypotheses (see later on), get activated.

• Now the DPN is created.

Basic system flow (2)

• A Human Agent (…) is added to each Fusion Agent.

• This Human Agent contacts the Callcenter and sends a query about the hypothesis.

• The Callcenter sends the query as an SMS to the appropriate human(s), and receives an answer.

• The network gets updated and the control room informed about the evidence.

System designDistributed Perception Network

Concept ManagerAgent

Yellow pagesagent

Callcenter

SMS gateway

EnvironmentSMS SMS SMSSMSSMS

S S S SS S S S S S S S SSMSSMS

Fusion agent

FA HA

SMS Gateway

BD

Concept Manager Agent (1)

• Responsible for DPN initialization.• Bottom up approach.• A sensor is monitoring its environment and sends its

data to its Sensor Agent.• At a certain moment the sensor data may provide

enough evidence for the sensor agent to set its value to true.

DPN

Environment

S S S S S S

SA SA SA SA

FA FA FA

C C

CMA

FA FA

CDPN

Environment

S S S S S S

SA SA SA SA

FA FA FA

C C

CMA

FA FA

C

Concept Manager Agent (2)

• It then sends a message to the Concept Manager.

• The Concept Manager can select all Fusion Agents that have the particular Sensor Agent as one of its possible children.

• An activation message is sent to these agents

DPN

Environment

S S S S S S

SA SA SA SA

FA FA FA

C C

CMA

FA FA

CDPN

Environment

S S S S S S

SA SA SA SA

FA FA FA

C C

CMA

FA FA

C

Concept Manager Agent (3)

• When the agents receive the activation messages they will spawn their own world models, using a top down approach.

DPN

Environment

S S S S S S

SA SA SA SA

FA FA FA

C C

CMA

FA FA

CDPN

Environment

S S S S S S

SA SA SA SA

FA FA FA

C C

CMA

FA FA

C

Concept Manager Agent (4)

• When there is enough evidence to determine with certainty that the agent concept is true then again a message is sent to the CMA.

• The process repeats untill top concepts are reached.

DPN

Environment

S S S S S S

SA SA SA SA

FA FA FA

C C

CMA

FA FA

CDPN

Environment

S S S S S S

SA SA SA SA

FA FA FA

C C

CMA

FA FA

CDPN

Environment

S S S S S S

SA SA SA SA

FA FA FA

C C

CMA

FA FA

C

Intermezzo: Certainty in the system

• When is a concept, represented by a Fusion Agent, uncertain?

• Rephrase: when are we maximally uncertain about a hypothesis?

• … when p(hypothesis) = 0.5

• Concept: ‘fire’

• Hypothesis: ‘there is a fire’

(Un)certainty curve: Uncert(p) = N(0.5, 0.25)(p)

Cert (p) = 1.0 –( Uncert(p) / Uncert(0.5) )Uncertainty above threshold means that

the hypothesis is uncertain

System design cont. Human Agents (1)

• The goal of a Human Agent is to remove uncertainty about the hypothesis of the Fusion Agent to which it belongs.

• Note: it already knows that Uncert(hypothesis) < threshold.

• It can percieve and update the Fusion Agent.

• It can communicate with the Callcenter.

Human Agents (2)

• A Human Agent can undertake one of two possible actions: he can contact the Callcenter, or he can choose not to do so.

DPN

Environment

S S S S S S

SA SA SA SA

FA FA

FA

Callcenter

HA

HA

HA

Human Agents (3)

• Assign a priority measure to a hypothesis: priority(hypothesis)=uncertainty(hypothesis)

• Do not contact if a lot of agents want to contact the Callcenter.

• utility of an agent i to contact the CC: utility(i) = priority(i) + ( 1 / length(queue) )

• Idea: always contact if the hypothesis is maximally uncertain and/or if the HA is the only one in the queue.

Human Agents (4)

HA :: CC Communication

• If a Human Agent’s utility is high enough, it connects to the Callcenter through a socket.

• The Callcenter is continuously listening to a port for requests.

• = Client server application.

• Supports distributed character of the system.

Callcenter (1)

• A priority queue is maintained to store the incoming queries.

• The Callcenter decides which people to contact.

• These people have to be located. We simulated this with a database.

Callcenter (2)

Callcenter

SMS gatewaySMS SMS SMSSMSSMSSMSSMS

Fusion agent

FA HA

SMS Gateway

BD

Callcenter (3)

• In the database also reside human properties: age, proffesion etc.

• Depending on the query, people are selected that are most likely to give a credible response: we prefer a doctor over an AI student for medical questions, and prefer AI students over 10 year olds in general (?).

• If the question is general, a simple broadcast is done to all people in the crisis area

Callcenter (4)

• Responses are also weighted: the response of a gas expert will be weighted heavily for a gas-query.

• If #‘yes’ > #‘no’, send ‘yes’ back to the Human Agent.