Seminar aus Informatik WS2005/2006

February 28, 2006

Multi-Agent Systems for Environmental Control & Intelligent Buildings

Report

Thomas Fuhrmann Bernhard Neuhofer

tfuhr@cosy.sbg.ac.at bneu@cosy.sbg.ac.at

Department of Computer Science

University of Salzburg

Universitat SalzburgInstitut fur Computerwissenschaften

Jakob–Haringer–Straße 2A–5020 Salzburg

Austria

Multi-Agent Systems for Environmental Control & IntelligentBuildings

Thomas Fuhrmann Bernhard Neuhofer

Department of Computer ScienceUniversity of Salzburg

A–5020 Salzburg, Austria

tfuhr@cosy.sbg.ac.at, bneu@cosy.sbg.ac.at

Abstract

This report deals with current research efforts in the field of building automation aka Intelligentbuildings and environmental control. Due to the huge amount of data that has to be processedin an intelligent building, AI methods such as Multi-Agent Systems are used for breakdown ofcomplexity and problem structuring. However it is not always clear why the agent approach isexplicitely chosen in the projects described in this report and what advantages it has comparedto conventional methods.

Contents

1 Motivation 21.1 Goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

2 Intelligent Buildings 32.1 Definition of an ”Intelligent Building” . . . . . . . . . . . . . . . . . . . . . . . 32.2 Automation of Tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32.3 Improvements in Efficiency and Quality of Service . . . . . . . . . . . . . . . . 4

3 Multi-Agent Systems (MAS) 53.1 Definition? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53.2 Why using Multi-Agent Systems for Intelligent Buildings? . . . . . . . . . . . . 5

4 Fuzzy Inferencing 74.1 Fuzzification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74.2 Rule evaluation & aggregation of the outputs . . . . . . . . . . . . . . . . . . . 84.3 Defuzzification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

5 UMASS Intelligent Home Project 95.1 Main Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95.2 Example: How to make coffee . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105.3 MASL Simulator Program . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

6 Energy Saving in Intelligent Buildings 126.1 MAS in this Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126.2 Field Bus Connection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136.3 Architecture of the MAS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

7 Adaptive Building Automation 157.1 System Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157.2 Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157.3 Distinct Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

1

Chapter 1

Motivation

Nowadays buildings are equipped with a huge amount of sensors and actuators (for example

temperature, moisture or radiation sensors). These sensors generate a immense amount of data

which require to be computed in near real-time, in order to take decisions right on time.

One logical aproach would be a functional and spatial distribution of tasks in order to reduce

complexity and computation time. On the other hand a distribution of tasks normally generates

overhead and a lot of redundant systems. This would be a waste of ressources and certainly

not the way to find a optimal solution using not more ressources than absolutely necessary.

1.1 Goals

The goals of intelligent buildings should be energy savings, cost reduction and an adaptation

to needs and preferences of the buildings inhabitants.

If you take these goals one step further you can also reach complete automation of all essen-

tial building functions without loosing control of any essential manual function (for example

emergency door control in case of fire) of the building.

2

Chapter 2

Intelligent Buildings

2.1 Definition of an ”Intelligent Building”

”The concept of an intelligent building is, and will probably remain, ill-defined. In its most

general sense it should mean a building that in some way can sense its environment, reach

decisions about the state of that environment and communicate those decisions. In practice

this should mean that a building can adjust some aspect of the interior or exterior environment

in response to a change in some other aspect of that environment.” (www.wikipedia.org [3])

2.2 Automation of Tasks

If you think about automation in an intelligent building you will probably first think about

automatic climate control or automatic access controls, but in an intelligent building you take

this thought one step further.

This step further can be a adaptation of temperature or moisture levels to the individual

preferences. The lights can be switched on at an indidual level of darkness or the windows can

be opened or closed automatically.

If you add a detection mechanism to the individual inhabitants you can even track a person

and adjust the current room to the preferences of this person or at least optimize the room to

a compromise which satisfies all current inhabitants.

These are only some examples how a user could directly experience an intelligent building but

there are a lot of hidden sensors and actuators which effect the efficiency of an building and

can not be experienced directly.

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2.3 Improvements in Efficiency and Quality of Service

In days of raising energy costs saving energy is fundamental. As a consequence if you use solar

energy and light for heating and lightning you can save energy. Also window blinds can be a

effective way of reducing costs for climate control even turning the light automatically on and

off can save energy.

An improvement in quality of service can be for example that your computer is already turned

on when you come to work so that you can start working immediatly, that your coffee is ready

or that your phone calls follow you automatically. But an intelligent building could also detect

when you are in a conference room with other people and as a consequence recognize that you

do not want to be disturbed by the phone.

There are uncountable other scenarios where an intelligent building could improve the quality

of service or the efficiency of the building by saving energy and as a consequence money.

4

Chapter 3

Multi-Agent Systems (MAS)

3.1 Definition?

There is no formal definition of an multi-agent system, only an agreement on the most common

features like multiple agents acting in one evironment, all agents have the same input, actions

affect the common evironment of all agents or communication between agents and probably

between agents and the environment.

On the other hand are there some definitions of a multi-agent system which are rather descrip-

tive like:

”An agent is a computer system that is situated in some environment, and that is capable of

autonomous action in this environment in order to meet its design objectives”(Wooldridge and

Jennings,1994)

Multi-agent systems can be claimed to include human agents as well. Human organizations

and society in general can be considered an example of a multi-agent system.

Multi-agent systems can manifest self-organization and complex behaviors even when the in-

dividual strategies of all their agents are simple.

3.2 Why using Multi-Agent Systems for Intelligent Buildings?

Most of the projects we have found concerning intelligent buildings use multi-agent technology

to simulate a building with sensors and actors. Why do they not use classical simulation tools

like for example desmo-j? [7]

The main reasons why multi-agent systems are used for simulating intelligent building envi-

ronments are:

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inaccessible: Nowadays it is quite hard to find buildings which can be used for research and

testing of new concepts. Nearly all buildings which are equipped whith sensors and actors

are controlled by classical software and a disturpance of the daily buisiness would be too

expensive.

non-deterministic: There is no simple fomular which can be used directly for optimisation.

non-episodic: Even slight variations in the events change the behavior of the whole system

and as a consequence is there hardly any repetition.

dynamic: Only the building itself is static all parameters can change.

continuous: All tasks are continous and not discrete.

reactivity: The agents in the simulation have to react on input from various sensors in real-

time.

pro-activeness: Agents should react even before a extreme or unwanted situation occures.

social ability: The agents have to work together in order to find a near optimal solution to

this optimization problem

Summing up you can say that using multi-agent systems for simulating intelligent buildings is

just one way to go. You can reach the same goals by using classical simulation techniques, but

multi-agent systems are a more natural approach.

6

Chapter 4

Fuzzy Inferencing

Fuzzy Logic[2] is an extension of classical first order logic from the discrete 0 and 1 to the con-

tinous range [0..1]. There are three steps when using fuzzy logic: fuzzyfication, rule evaluation

& aggregation of the outputs and defuzzyfication.

4.1 Fuzzification

The first step if you want to use fuzzy logic is that you fuzzificate your input parameters.

In Figure ?? you can see how fuzzification ist done. You have to transfer your value using the

fuzzification functions into one or more values from the range of 0 to 1 in order to use fuzzy

inferencing.

In the left diagram the value of 35 is transformed into low=0.5 and medium=0.5. The other

diagram shows the transformation of a value into different values of the categories cold and

warm.

Figure 4.1: Fuzzification

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4.2 Rule evaluation & aggregation of the outputs

The next step is evaluating the logical calculus using fuzzy logic where AND, OR, and NOT

operators of boolean logic exist in fuzzy logic, usually defined as the minimum, maximum, and

complement.

You can also aggregate all your outputs in order to receive a single output. In the example

shown in Figure 4.2 the whole colored area is transformed into a single value by computing the

center of gravity.

4.3 Defuzzification

The final step of fuzzy inferencing is the defuzzifcation. In this step you convert the fuzzy

output back into classic logic.

Figure 4.2: Defuzzification process

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Chapter 5

UMASS Intelligent Home Project

The intelligent home project at the UMASS[1] multi-agent systems lab is an application of

multi-agent systems technology to the problem of managing an intelligent environment. They

have implemented a sophisticated simulated home environment and populated it with dis-

tributed intelligent home-control agents.

Currently only a simulation exists and no real world testing environment which could verify

the results of the simulation.

The focus of the project is on resource coordination and on temporally sequencing agent activ-

ities over shared resources.

The UMASS-project includes agents like an intelligent WaterHeater, CofeeMaker, Heater, A/C,

DishWasher, etc., and a robot. Each agent is associated with particular appliance.

5.1 Main Objectives

The main goals of this project are:

• Examine the intelligent home domain as a general application

• Understand the distributed control issues of this particular multi-agent application

• Apply the TÆMS1 domain-independent task modeling framework to a new domain and

evaluate its use in the rapid development of a new multi-agent application

• Test and refine this multi-agent simulation environment

• Test and refine this java-based generic agent construction framework1Task environment centered simulation[4]

9

5.2 Example: How to make coffee

As you can see in Figure 5.1 the simple task of making coffee can be quite difficult. Figure 5.1

shows how the UMASS-project makes coffee and calculates the quality of the coffee.

D (100% 4.0)

Fill-Water

Get-Hot-Water Get-Cold-Water

Acquire-Ingredients

C (100% 2.0)

Make-Coffee

Hot-Coffee

Get-Coffee

Use-Coffee-Instant

Q (100% 70.0)q_min

Acquire-Beans

q_exactly_one

C (100% 1.0)D (100% 2.0)

Q (100% 120.0)C (100% 6.0)D (100% 15.0)

Mix-And-Filter

Q (100% 150.0)C (100% 1.5)D (100% 4.0)

C (100% 5.0)D (100% 1.0)

D (100% 2.0)

Method

HotWater

Electricity

Noise

Enables

Task

Brew-Coffee

Buy-Beans-From-StarbucksUse-Frozen-Beans

D (100% 3.0)C (100% 2.0)Q (100% 120.0)

Grind-Beans

q_exactly_one

Acquire-Ground-Beans

q_exactly_oneq_min

q_min

q_exactly_one

Q (100% 125.0)C (100% 6.0)D (100% 4.0)

Q (100% 80.0)

Q (100% 115.0)

Q (100% 130.0)C (100% 3.5)

Figure 5.1: How does the UMAS-Project make coffee?

If you take a closer look at the Figure 5.1 you will notice the word q min which means that

the resulting quality will be the minimum of the qualities of the two or more child processes.

The word q exactly one means that exactly one children can be used to get a correct result

and that the resulting quality is the quality produced by the selected children.

If you look at the method of Grind-Beans you can see that this step in the creation of coffee

generates noise and requires electricity. The Q, C and D values mean that the produced quality

is 120.0, the costs are 2.0 and the duration is 3.0 if you complete 100% of this step.

Each task in the UMASS-project is modelled by a similar tree and so the quality and costs of

each action can be easily computed. The difficulty in this optimsation problem is the ballance

between costs and quality.

10

5.3 MASL Simulator Program

In Figure 5.2 can see the MASL2 simulator program running a simple test run of a small

apartment. The first window on the top-left ist the event window, here you can see which

events are currently active and need to be processed by the agents.

The Window on the top-right displays a summary of the current simulation and the position

of each agent in the simulation. Also the temperature and other statistics are displayed in this

window.

In the middle-left window you can see the detailed graph of an agent. In this case the behavoir

graph of the heater is displayed.

In the bottom-right corner window you can trigger actions like turning an agent on or off or

removing an agent from the simulation while having a test run.

The window on bottom left displays a histogram of the number of messages the agents have

exchanged at a certain point of simulation time.

Figure 5.2: The main simulation window

2Multi-Agent Systems Laboratory

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Chapter 6

Energy Saving in Intelligent

Buildings

In [5] the usefulness of an software agent system that controls a small office building using

existing electrical devices is examined. The objectives of this project being a part of the ISES

project (Information/Society/Energy/System) are both energy saving and enhancement of cus-

tomer value. Energy saving is performed by controlling lights, heating, ventilation. Enhanced

customer value is the system’s response by adjusting light intensity, temperature to the peoples

desires. However these two objectives are generally competing since increasing the temperature

in a room for example to adapt to a person’s individual preference does not necessarily help in

saving energy. So the system’s behavior will reflect a competition between energy saving and

adapting to people’s personal preferences.

6.1 MAS in this Scenario

The team of this project sees the term MAS in sense of modelling desired services by societies

of agents that form a distributed system. Comunication is done via the existing electrical

infrastructure. The MAS in this scenario consists of software agents that control different parts

of the building as well as different parts of the environmental conditions in the building. Other

agents represent the people in the building in order to adapt the environmental parameters

to each individual’s preferences. The whole system is fully transparent to the people in the

building so that interaction with the system should not be necessary. The authors state that

the use of an agent based technology for this type of application has a number of advantages like

scalability and re-configurability. New agents should be able to enter the system dynamically

without disturbing the operation of the system as a whole. Also adding new policies should

be much easier. The authors of [5] mention that the use of the multi-agent approach was

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motivated by the close mapping between the entites of the application domain i.e. the entities

in the building and the software entities. However one could argue that this could also be

achieved with good modularization and is merely used as another method of structuring the

system. So using an MAS technology only for this reason would not make sense. A more

important factor for using an MAS and therefore AI mehods in this scenario is the concurrent

and non-deterministic nature of the activities inside the building. Here the use of an MAS with

entitities that can perform tasks autonomously and communicate with each other seems to be

much more suitable.

6.2 Field Bus Connection

Modern office buildings are often equipped with a fieldbus system where all electrical devices

are connected to. The test site for the ISES project uses a bus system based on LonWorks

technology. All electrical equipment is connected to the LonWorks field bus and allows com-

munication between the different elements via the propietary LonTalk protocol. Some of these

devices are sensory devices such as temperature measurement, light intensity and presence de-

tectors that form a so called active badge system. This system makes it possible to exactly

identify which persons are in each room. The other devices called actuators do not only provide

information about their state (i.e. environmental parameters) to the control system but allow

to change their state and thus the state of the building. Such actuator devices are lamps,

radiators and electrical blinds.

6.3 Architecture of the MAS

The MAS approach was chosen because of the close mapping between agents and building

entities. Each agent is given a number of rules which define the building conditions. Communi-

cation between these agents arises when events inside the building occur e.g. a person moving

from one room to another. For the whole application we can find four main types of agents:

Personal Comfort (PC) Agents These are agent that can reside on the individuals’ per-

sonal computers and are responsible for adapting the room conditions to the persons’

preferences. However they do not model a person’s behavior, but they only try their best

to fullfill the person’s individual preferences.

Room Agents are responsible for controlling all aspects of a room under the prime objective

of saving as much energy as possible

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Environmental Parameter (EP) Agents control different environmental parameters of room

and are therefore a link between the room agents and the LonWorks infrastructure. The

main task an Environmental parameter agent tries to fullfill is to set and keep the pa-

rameter values decided by the Room agents.

Badge System (BSA) is responsible for identfieing and tracking the movements of the people

inside the building

When a person moves between different rooms the BSA first informs the appropriate PC agent

which again informs the two room agents involved and tells the RA corresponding to the room

entered about the person’s preferences. The Room Agents then calculate the new environmental

settings based on energy saving considerations and the person’s preferences. As we go one step

further the EP agents corresponding to the room sends messages to the actuators via the

LonWorks bus and set the environmental parameters.

As mentioned above the whole system can be but does not necessarily has to be distributed

locally in the building. The PC agents can reside on the people’s desktop computers, the room

agents can reside on different machines inside or outside the corresponding rooms and the EP

agents can even reside in the hardware connected to the devices.

Currently the project described is still in simulation phase. However the transition to a physical

implementation in a test building is in planing.

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Chapter 7

Adaptive Building Automation

Another multi-agent approach for the design of an intelligent building is called Adaptive Build-

ing Automation or AHA. The basic entity that this systems is based upon is a room. All data

processing is done in the context of a room or collection of rooms and the main objective of

the system is to satisfy the inhabitants and to provide full automation so that the inhabitants

do not have to interact with the building.

7.1 System Overview

There is usually a huge amount of data that has to be processed and analyzed in an intelligent

building environment. As conventional techniques did not seem satisfy for this apllication

domain the authors of [6] choose the approach of distributed autonomous agents where each

of them is able to make its own decisions based on the input data provided. As a common

method for this decision making process the authors have used fuzzy logic and fuzzy inferencing

described in chapter 4. Each agent has its own goal in the environment but to fullfill the desired

objectives all agents have to act in a cooperative manner.

7.2 Architecture

The main logical concept of AHA is a room. All sensory input (except inputs like weather data)

is related to a room. However this mapping is only done in a logical manner. The different

agents controlling different rooms are not distributed physically embedded agents but reside on

one machine. Here again comes the question of the usefullness of the MAS as a whole. Software

parts can communicate with others so there is not necessarily the need of using agents. The

strength of agents is to find a general solution to a problem by solving smaller parts of the prob-

lem space. However the agent based approach becomes more and more suitable when attaching

15

importance to flexibility and scalability. Adapting the system to changing building conditions

or even new buildings is an important advantage of the agent based solution. Physically the

systems contains of two networks the first being the agent server(s) that are connected to a

LonWorks field bus via ethernet. The important part here is a gateway that is connected to

both networks to enable AHA to run on the computer network without direct access to the

field bus. Similar to [5] the system is realized as an hierarchical collection of agents that form

the MAS. There is no central agent but all agents act autonomously. The MAS is structured

into five layers in order to reduce the amount of data the agents have to deal with. Higher

levels have to deal with less data than lower ones. However the position in the layer model does

not limit the agents in communicating only with agents that are positioned one layer above or

beneath.

Figure 7.1: The layer structure of AHA

16

The different types of agents in the system are as follows:

Bus Agent connection to the LonWorks field bus

RoomDisplayAgent displays information about the environmental parameters

Control Agent processes data and takes decisions based on fuzzy inferencing; also executes

these decisions

History Agent for logging purposes

Boss Agent can create new instances of agents dynamically (e.g new rooms in the building)

7.3 Distinct Features

What makes this architecture different from other projects is mentioned in [6]:

• “AHA is based on a multi agent architecture that is fully implemented in software (soft

agents)”

• “The scope of the system is a single building and not a particular room”

• “Buildings are regarded as a collection of rooms. None of these rooms is conceptually

different from the others, there is no such thing as a meeting room or an office.”

Due to the complex nature of this environment classical rule-based methods often fail to pro-

vide good solutions (since there usually is no exact solution, but only a more or less optimal

one). Therefore the agent-based approach was chosen with the following agents’ behavior in

mind:

The agents should

• “have the ability to learn and predict a person’s needs and adjust the system to meet

this person’s needs”

• “tell this conclusions to other agents”

• “do such learning on a wide set of imprecise data”

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Bibliography

[1] Victor Lesser, Michael Atighetchi, Brett Benyo, Bryan Horling, Anita Raja, Regis Vincent,

Thomas Wagner, Ping Xuan and Shelley XQ. Zhang A Multi-Agent System for Intelligent

Environment Control. UMass Computer Science Technical Report, 1998-40, March 29,

1999.

[2] http://en.wikipedia.org/wiki/Fuzzy logic, 22 February, 2006

[3] http://en.wikipedia.org/wiki/Intelligent building, 22 February, 2006

[4] Keith S. Decker. Task environment centered simulation. In M. Prietula, K. Carley, and

L. Gasser, editors,Simulating Organizations: Computational Models of Institutions and

Groups.AAAI Press/MIT Press, 1996

[5] Boman, M.,Davidsson, P. et al. Energy Saving and Added Customer Value in Intelligent

Buildings. M.Sc. Thesis, Dalhousie University, Halifax, Nova Scotia, Canada, 1995.

[6] Rutishauser Ueli and Schfer Alain. Adaptive Building Automation. Tech. Report, Institute

of Neuroinformatics, University of Zurich, 2002.

[7] http://www.desmoj.de/, 28 February, 2006

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