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Seminar aus Informatik WS2005/2006
February 28, 2006
Multi-Agent Systems for Environmental Control & Intelligent Buildings
Report
Thomas Fuhrmann Bernhard Neuhofer
[email protected] [email protected]
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
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
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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.
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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.
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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.
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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]
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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.
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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
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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
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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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