AGENT BASED FRAMEWORK TO SIMULATE INHABITANTS’

BEHAVIOUR IN DOMESTIC SETTINGS FOR ENERGY

MANAGEMENT

Ayesha Kashif

1,2

, Xuan Hoa Binh Le

, Julie Dugdale

and Stéphane Ploix

LIG – Laboratoire d'Informatique de Grenoble, 110, Av de la Chimie, 38400, Saint Martin d'Hères, France

G-SCOP; ENSGI – INPG, 46, Avenue Félix Viallet, 38000, Grenoble, France

Keywords: Multi agent system, Inhabitants’ dynamic behaviour, Energy efficiency & management.

Abstract: Inhabitants' behaviour is a significant factor that influences energy consumption and has been previously

incorporated as static activity profiles within simulation for energy control & management. In this paper an

agent-based approach to simulate reactive/deliberative group behaviour has been proposed and

implemented. It takes into account perceptual, psychological (cognitive), social behavioural elements and

domestic context to generate reactive/deliberative behavioural profiles. The Brahms language is used to

implement the proposed approach to learn behavioural patterns for energy control and management

strategies.

1 INTRODUCTION

Europe’s energy consumption within buildings is

40% of the total energy (two-thirds of this is in

heating and cooling), however a major portion up to

90% is needlessly wasted (heel, 2009). By the year

2030, 70% (6 billion) of the world’s population will

live in urban areas resulting in huge sustainable

housing and energy demands. Hence the associated

energy loss from buildings is emerging as a potential

crisis (world urbanization prospects, 2007). A

solution to address this problem is energy efficiency

(saving from current energy waste) which is

cheapest, cleanest and immediately available, cost-

effective energy (ogilvie, 2009).

Energy control and management for heating and

cooling and lighting, etc. is an active research area.

The focus is for new buildings to comply with low

energy consumption standards and for renovated

buildings to improve energy efficiency as proposed

by the Euro ACE and European National Strategy

(Jensen et al., 2009). Centralized and distributed

approaches in buildings for power management

solutions have also been proposed to improve energy

efficiency, (Ha et al., 2006), (Abras et al., 2006). We

argue that understanding inhabitants’ behaviour is

the key for energy consumption and saving.

Inhabitants’ behaviour can either optimise energy

utilization, taking into account comfort needs, or it

can needlessly waste energy. Energy waste related to

human behaviour is not yet fully explored for energy

efficiency. The literature suggests that behaviour

strongly influences energy consumption patterns and

is an important factor for energy waste reduction in

buildings (Raaij and Verhallen, 1982), (Andersen et

al., 2009). Various surveys, studies and energy

audits have been conducted to analyze how

behaviour is affected by certain factors and how it

affects energy consumption (Seryak and Kissock,

2000), (Ouyang and Hokao, 2009), (Masoso and

Grobler, 2009). (Mahdavi and Proglhof, 2009)

conducted a study in order to find the user control

actions taking into account indoor/outdoor

environment. (Bourgeois et al., 2006) developed a

sub-hourly occupancy-based control model

(SHOCC) to track individual instances of occupants

and occupant controlled objects to investigate

lighting energy use in a single occupancy building

using ESP-r

. (Dong and Andrews, 2009) developed

an event based pattern detection algorithm for

sensor-based modelling and prediction of user

behaviour. They connected behavioural patterns

(Markov model) to building energy and comfort

management through EnergyPlus simulation tool for

energy calculations.

ESP-r is an integrated modelling tool for the simulation of the

thermal, visual and acoustic performance of buildings and the

assessment of the energy use and gaseous emissions.

190

Kashif A., Binh Le X., Dugdale J. and Ploix S..

AGENT BASED FRAMEWORK TO SIMULATE INHABITANTS’ BEHAVIOUR IN DOMESTIC SETTINGS FOR ENERGY MANAGEMENT.

DOI: 10.5220/0003150301900199

In Proceedings of the 3rd International Conference on Agents and Artiﬁcial Intelligence (ICAART-2011), pages 190-199

ISBN: 978-989-8425-41-6

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

The models discussed above focus on user

behaviour in non-domestic spaces such as offices

and they concern single users rather than group

reactive/deliberative behaviour. Simulations based

on static profiles or single user behaviour are limited

in extending results to real life. A better

management that coordinates and orchestrates the

use of all kinds of energy according to inhabitant’s

needs and comfort remains an important progress

factor. In this paper we focus specifically on

domestic situations and model dynamic

(reactive/deliberative) group behaviour which we

believe is the key for reliable simulation in energy

efficiency. The purpose of the proposed approach is

to identify the sensitivity of behaviour for energy

control and management which shall help in

developing the smart environments as well as testing

the design of new buildings or houses more suited to

humans according to their behaviour. A smart

environment is one that is able to acquire and apply

knowledge about environment and its inhabitants in

order to improve their experience in that

environment (Cook et al, 2007). A simulation has

been run in order to access human behaviour with

energy consumption which otherwise cannot be

done without some experimentation.

The conceptual framework proposed in this

article includes two components: (i) a logical

component for inhabitants’ dynamic group

behaviour (reactive/deliberative) simulation and (ii)

a physical component for energy calculations. An

agent based approach is used to model humans

interacting with their environment in the proposed

logical component. An agent based approach is well

suited since agents are a natural and intuitive way to

model humans and their characteristics and are a key

towards implementing group behaviour. Agents like

humans evolve in the environment, perceive it and

act accordingly.

The research objectives of this work are to

dynamically simulate user behaviour in domestic

settings, and further identify the context, beliefs and

facts that impact energy related behaviour. The

proposed framework will help in developing energy

efficient strategies to be implemented through social

campaigns, ubiquitous computing or centralized/

distributed approaches.

This work is part of the SIMINTHEC

(SIMulation and INteroperable software tools for the

management of THermal and EleCtrical energy in

buildings) project. The goal of SIMINTHEC is to

design a multi-simulation environment to improve

energy management in buildings by validating and

improve energy-saving policies and programs. It

includes five modules: 2 modules concerned with

thermal and electrical aspects, 1 module on energy

saving policies and control algorithms, 1 module on

inhabitants’ behaviour simulation and 1 module for

predicting the outdoor environment. Proposed agent

based framework to simulate dynamic group

behaviour, supports the “inhabitants’ behaviour

simulation” module, circled in Fig.1 with

interoperability among all modules.

ent

Set Points

emperat

re & H

idit

Prof

iles

hysical Values

Figure 1: Interoperability between modules in

SIMINTHEC.

2 LITERATURE REVIEW

The following section covers three aspects:

behaviour influence on energy consumption, home

context and Human Behaviour Representation

(HBR) models for possible integration in reactive/

deliberative group behaviour simulation.

There are multitude of factors of human

behaviour that influence energy consumption. For

example public information on the energy problem,

energy supply and energy efficiency, energy related

personal interests, economical differences, home

characteristics (no of rooms, degree of insulation),

lifestyle consciousness about energy saving and

environmental problems, social norms and lack of

knowledge about energy use (Raaij and Verhallen,

1982, Ouyang and Hokao, 2009).

A survey conducted by (Andersen et al., 2009)

showed that window opening, heating, lighting and

solar shading behaviour of occupants is affected by

gender, perceived illumination, noise level and air

quality. (Seryak and Kissock, 2000) conducted a

study on university residential houses and showed

that the same house occupied during 2 academic

years by different occupants show different energy

consumptions because of behavioural differences.

(Masoso and Grobler, 2009) conducted an energy

audit on six randomly selected buildings in Africa.

The results showed that more energy is consumed

during non working hours than during working

hours because of the occupant's behaviour of leaving

AGENT BASED FRAMEWORK TO SIMULATE INHABITANTS' BEHAVIOUR IN DOMESTIC SETTINGS FOR

ENERGY MANAGEMENT

191

lights and other equipment on at the end of the day.

(Ueno et al., 2006) presented an on-line energy

consumption information system to make the

occupants aware of the impact of their energy

consuming behaviour of different appliances, power

and gas consumptions of the whole house, room

temperature, comparison with other houses and

comparison with past data. The system helped in

reducing power consumption of houses by 18% at

the end of the study.

In addition to behaviour, context is another

important factor affecting the energy related

activities of occupants. “The context of a task is the

set of circumstances surrounding it that are

potentially of relevance to its completion”

(Henricksen, 2003). In context aware systems the

contextual elements necessary to represent

behaviour are categorized as individuality (state),

activity (human needs expressed as ‘what’ and

‘how’), location (spatial arrangements) and time

(current time or any virtual time, working hours,

weekends, intervals) and relations (social relations

and functional relations) (Zimmermann et al., 2007).

In the home environment user behaviour is

considered as one of the most important contextual

factor amongst others including time, space,

environment and object (Ha et al., 2006). These

authors presented a user behaviour modelling

approach (5W: what, when, where, why & who and

1H: how) by mapping it in a home context (user,

time, object, space & environment) (

Fig.2).

Figure 2: 5W1H approach to map user behaviour in the

home.

It is evident from the above studies that human

behaviour is the most important factor affecting the

energy utilisation in buildings. In an urge to study

this most important factor in more detail, to find out

its different aspects affecting energy related

activities directly or indirectly and to find a way to

represent it for energy control and management, a

study of existing human behaviour representation

models has been conducted.

HBR models capture the covert and overt human

behaviour patterns and represent them in some way

using some representation mechanism. Most of the

HBR models share the aspects of both cognition and

performance. HBR models were analysed to find

those that could represent reactive/deliberative and

group behaviour including context elements. Atomic

components of thought (ACT) (Anderson et al.,

2004) focuses on cognition (thought processes),

perception and motor elements. (Freed, 1998) &

(Firby, 1989), suggested Architecture for procedure

execution (APEX) to model human behaviour in

complex, dynamic environments, but focus only on

individual tasks. (Sloman, 2001) presented

Cognition and affect project (CogAff) that captures

the reactive, deliberative & reflective mechanisms.

Cognition as a network of tasks (COGNET)

(Zachary et al., 1998), mainly focuses on cognitive

behaviour of humans, assuming that humans are

capable of doing multiple tasks simultaneously.

(Card et al., 1983) and (Kieras and Polson, 1985),

proposed cognitive complexity theory (CCT) which

is a simple model of cognition as it represent human

performance only on the sequential tasks and show

how humans use their task knowledge to interact

with the devices. Concurrent activation-based

production system (CAPS), (Thibadeau et al., 1982)

and (Just et al., 1999), is a production system where

a declarative knowledge base consists of facts

having a numerical activation value. Production is

fired when an element is matched with the condition

and the activation value exceeds a specific threshold.

(Eggleston et al., 2000) presented the Distributed

cognition (DCOG) model, according to which

cognition is distributed across the environment.

Agents having different skilful behaviour use

different strategies to accomplish the same task and

environment does affect individual performance.

Executive process/interactive control (EPIC),

(Kieras and Meyer, 1995), focuses on perceptual,

cognitive and motor processes that represent the

procedures required to perform complex tasks. It

also captures multitasking. Man-machine integrated

design and analysis system (MIDAS), (Corker and

Smith, 1993), focuses on human system interactions.

It makes an assumption that the “human operator

can perform multiple, concurrent tasks, subject to

available, perceptual, cognitive and motor

resources” (Pew and Mavor, 1998). Micro systems

analysis of integrated network of tasks (Micro

Saint), (Pritsker et al., 1974), include task as a basic

element, divided in subtasks until an elemental level

is reached. It also uses operator oriented concepts to

accomplish tasks as a mission. However it does not

capture the psychomotor element of human

behaviour. (Deutsch et al., 1997) and (Young and

Deutsch, 1997), suggested Operator model

architecture (OMAR) with an assumption that

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human behaviour is proactive and reactive where

tasks occur concurrently within and among multiple

operators. State, operator, and result (SOAR) (Laird

and Newell, 1983), states that behaviour is captured

as a search or movement through the problem space

at a particular time and a goal state which represents

a solution for the problem. The knowledge is

modelled in terms of goals, states and operators,

where operators are used to change or transform the

state of the system. Business redesign agent-based

holistic modelling system (Brahms), (Sierhuis et al.,

1999), (Sierhuis et al., 2007), (Clancey et al., 1998)

& (Seah et al., 2005) is a modelling/simulation

environment to analyze work practices in

organizations and represents people, things, places,

behaviour of people over time, tools and artefacts

used, when they are used. It focuses on

communication between co-located and distributed

people to support social behaviour.

Brahms supports social and behavioural elements

necessary for dynamic group behaviour, however the

objective for logical simulation as presented in

section-1 is to find a model which can map human

behaviour process for reactive/deliberative group

behaviour and the context. The mapping between

user behaviour elements, context and Brahms is

presented in

Fig.3 below:

Figure 3: 5W&1H approach mapped to Brahms.

Workframes (activity model) and throughtframes

(knowledge model) are key elements in Brahms.

Thoughtframes are used to model the reasoning

behaviour of agents and are represented as

production-rules creating new beliefs of agents or

objects whereas workframes (rule-based) perform

agents and objects activities (simple or composite).

Brahms includes an agent model that represent

agents along with group hierarchy, and a

communication model to exchange beliefs about

agents and objects. It also provides means to model

locations and objects (geographical and object

models), that are important to establish the

environment in which agents operate. Brahms can be

used to model human beings interacting with a

complex habitable environment as powerful, active,

intelligent agents rather than passive participants for

energy efficiency and it can represent the

complexities found in real world human-

environment interaction scenarios. The literature

shows that behaviour inclusion within energy

control and management is focused on either static

profiles or predictive models (sensor based

inhabitants’ occupancy detection). However they

are based on single user interactions and do not

embed reactive/deliberative decision making. In this

paper inhabitants perception, cognition and

reactive/deliberative group behaviour is simulated

using home context (5W1H) and mapping it to

Brahms. It provides an opportunity to learn context,

beliefs and activities that influence energy

consumptions and could play significant role in

energy efficiency within domestic settings. Our

proposed approach is different from the existing

research to the extent that we have demonstrated

dynamic behaviour simulation and results obtained

shall be applicable to the real life situations.

3 PROPOSED FRAMEWORK

To simulate inhabitants’ reactive/deliberative group

behaviour an integrated definition (IDEF) model

with three levels of abstraction

(Fig.4, Fig.5 and Fig.6)

is proposed:

Figure 4: Behaviour simulation for energy efficiency.

IDEF models processes as functions with inputs

(left arrows), outputs (right arrows),

controls/constraints (top arrows) and means/methods

(bottom arrows) at different levels of abstractions.

Function A0 (

Fig.4) represents the highest level of

abstraction where 5W1H (domestic context) and

initial beliefs serve as input, comfort/cost criteria as

control, user behaviour and power management as

output and behaviour base, Inhabitant’s behaviour

and physical components and connector to

interoperate the outputs of these two components as

means/methods. Inhabitants’ in the 5W1H model fed

as input to function “A0” correspond to the agents

and their surrounding environment.

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Sub-functions A1 (simulate dynamic inhabitants’

behaviour) and A2 (calculate energy performance)

as presented in

Fig.5. These represent the conceptual

framework of the abstract function A0. It is the

second level of functional abstraction towards

learning context and beliefs/facts from energy

related group behaviours within domestic settings:

Figure 5: Conceptual framework for behaviour simulation.

(i) Simulate dynamic inhabitants’ behaviour

(A1):

The ‘Simulate dynamic inhabitant’s behaviour’

component is the core element to simulate

reactive/deliberative group behaviour using an agent

based approach. 5W1H (context), initial beliefs and

facts (single user activity profiles) are inputs. “A1”

is implemented and simulated using Brahms

language/environment for dynamic group behaviour

scenario as presented in section 4 and its output

(dynamic user behaviour) serves as input to sub-

function A2 for energy calculations.

(ii) Calculate energy performance (A2):

The Physical component uses cost/comfort criteria

and the inhabitants’ behaviour/physical component

connector to calculate cost integrated with

behaviour. The Behaviour base serves as a data

structure to store dynamically generated behaviours.

The objective is to identify the context, beliefs and

facts that influence energy consumption patterns to

formulate energy control and management

strategies. This part is under implementation and is

not presented in this article.

The functional description of sub-function “A1”

as reactive/deliberative inhabitants’ group behaviour

is detailed below along with the sub-functions in

Fig.6. Since our approach is based on Belief Desire

Intention (BDI) agents, we can keep track of the

initial and changing beliefs of agents about

contextual elements, such as the state of objects

(what), inhabitants (who), physical location of

inhabitants (where) and current activities (how).

(i) Get context information (A1):

This function gets the information of three important

context elements i.e. inhabitant (who), object (what)

and location (where). The inhabitants are

represented by agents and it captures their beliefs

and facts, e.g. who is the inhabitant/agent, what are

the different characteristics of the inhabitants/agents

and how they perceive the environment around them

etc. The second important context element is the

‘physical location’ of inhabitants/agents and objects

(physical objects and appliances) in domestic

settings. The third context element ‘object’ provide

information about the appliance in use by the

inhabitant or that are involved in some activity along

with its state (on/off etc.). Output from this function

serves as input to the function ‘Update knowledge

base’.

(ii) Update knowledge base (A2):

This function takes ‘Knowledge base’ as its mean

which corresponds to memory where all the beliefs,

facts and context information is stored and updated.

It takes initial beliefs, facts and context information

from the ‘Get context information’ function.

Changed beliefs and facts are updated every time

some activity is performed by the ‘Perform activity’

function or based on some new beliefs from the

function, ‘Generate psychological state’. Output of

updated beliefs and facts serve as input to functions

‘Generate psychological state’, ‘Compute activity’

and ‘Calculate energy and save context’.

(iii) Generate psychological state (A3):

This function corresponds to human psychology

which varies from individual to individual based on

certain beliefs and facts. The psychological state is

generated based on the beliefs and facts and context

elements available to it from the “Update knowledge

base” function. It also captures two important

aspects of humans i.e. “feel and want”. For example

based on the fact that the temperature rises slowly in

the room, a person starts feeling hot when a certain

amount of temperature is reached and may want to

open the window based on his belief that the

temperature is very high. This belief will further

influence the “Compute Activity” function for the

selection of appropriate activity. It takes the social

behaviour from the function ‘Compute social

behaviour’ as control/constraint to generate changed

beliefs based on some social influence. For example,

having a belief that temperature is very high,

inhabitant does not open the window due to the fact

that other people present in the room don’t feel as

much hot and do not want him to open the window.

Every time the beliefs are changed, they are updated

in the “Update knowledge base” function.

(iv) Compute activity (A4):

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Compute activity function represents a reactive

behaviour and is associated with the selection of an

appropriate activity like single user activity without

deliberation, single user activity with deliberation or

group activity with or without deliberation based on

the changed beliefs and facts as input from ‘Update

knowledge base’ function and psychological

influence as control from ‘Generate psychological

state’ function.

Since the inhabitants select some activity to be

performed based on the context under the

psychological influence, this function selects which

activity (single user/group) is to be performed. It

takes as input the beliefs, facts and context

information from the “Update knowledge base”

function and inhabitant who will be involved in the

activity from the “who” model. The ‘why/how’

model serves as means which contains the

information about activity. If selected activity is a

single user activity and does not require deliberation,

it is directly fed to the ‘Compute activity time’

function otherwise it is submitted to the ‘Compute

deliberative behaviour’ function. In case of group

activity it is always fed to the function ‘Compute

social behaviour’ for group agreement. The ‘why’

model depicts the reason or cause of computing

some activity. There could be certain causes to select

some activity which affect the energy consumption

patterns of inhabitants. These causes can be

categorized into primary and secondary causes. For

example a primary cause to turn on the electric lamp

in the corridor is that the inhabitant is passing by

there and it’s dark, whereas the secondary cause may

be some aesthetic sense.

(v) Compute social behaviour (A5):

Social behaviour of inhabitants significantly affects

the energy consumption patterns in domestic

settings. For example inhabitants having dinner

together may consume less energy than everybody

going to the kitchen and turning on the light and hot

plate at different times. This function takes input

from the ‘Compute activity’ function in case of a

group-activity and uses ‘who’ model as input which

will let this function know about the inhabitants who

will be involved in the group activity to perform

group agreement. Output in case of group agreement

could be fed to the function ‘Compute activity time’

if deliberation is not required otherwise it serves as

input for the function ‘Compute deliberative

behaviour’. The psychological state of inhabitants

affects the social behaviour which corresponds to

some group agreement or no group agreement.

Similarly the social rule of having dinner together

which is stored as agent beliefs can be bypassed by

some agent or all of them upon the perception of

some new facts and beliefs.

(vi) Compute deliberative behaviour (A6):

The deliberative behaviour of an agent is caused due

to some changed beliefs and facts which influence

the performance of the selected activity. This

function captures deliberation on different elements

like cost, comfort etc. for the selection of an

appropriate alternative activity. Deliberation is a

reasoning mechanism where an inhabitant decides

which activity to be performed keeping in view the

consequences of all possible choices. Deliberative

behaviour affects energy consumption e.g. having

multiple options to lower the temperature in the

room as it’s very hot inside, one of the inhabitants

believes that opening window can be a good solution

and moves to open the window. He then realizes a

storm outside. This new perception of a bad weather

outside by the agent at the ‘Compute activity’

function will update the changed beliefs and facts in

the “Knowledge base”. Based on the changed facts

and beliefs in the “Knowledge base” which serves as

means to this function or the past experiences which

are saved in the “Behaviour base” an inhabitant may

change his mind to turn on the air conditioning

system instead. The choice of alternative actions

based on cost, comfort, information etc. is stored in

a database called ‘behaviour base’ which could help

the inhabitant for future choices where he could

maximize the comfort while minimizing cost if he

likes to do so.

The selected activity after deliberation is finally

sent to ‘Perform activity’ function.

(vii) Compute activity time (A7):

This function computes the time when some activity

is to be performed by the inhabitants, e.g. the start

and end time etc. It computes activity duration and

sends this information to ‘Perform activity’ function.

It receives activity information as input from the

‘Compute activity’ or ‘Compute social behaviour’

functions and the timing information from the

“when” model, however activity time is computed

only upon the receipt of the activity information.

(viii) Perform activity (do/how) (A8):

Based on the single, group, reactive, deliberative

behaviour the activity is performed by this function

and the information is used to calculate the energy

consumption of this activity. It takes as input, a

single/group activity and its associated time from

‘Compute activity time’ function and outputs the

changed beliefs and facts to ‘Update knowledge

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Figure 6: Functional model for dynamic behaviour simulations.

base’ and activity completion information to

‘Calculate energy and save context’ functions

respectively.

It is important to note here that activity

performed is not physically executed but simulated

for execution and is represented as start and end

time. Upon completion of the activity i.e. end time,

outputs are further submitted as respective inputs.

(ix) Calculate energy and save context (A9):

This function collects information about the

performed activity and other context elements

(beliefs and facts) from ‘Perform activity’ and

‘Update knowledge base’ functions and calculates

the energy consumed after performing the activity.

Information about the activity performed, context

elements and energy consumed is also saved in the

behaviour base which could further be utilized to

make choices based on cost/comfort criteria. Finally

the dynamic behaviour and power solution is

provided as output. The power solution will provide

a series of calculated energy requirements on

varying dynamic group behaviour within domestic

settings. It will help in identifying min/max energy

demand to balance the supply and demand equation

as well.

Activity information fed from ‘Perform activity’

function consists of name, start time and end time.

‘Calculate energy cost and save context’ function in

the presence of this information and beliefs and facts

calculates energy using activity duration and

appliances with associated energy costs. This

function calculates energy performance based on

theoretical and actual energy costs, theoretical

energy cost is computed based on static activity

profiles and actual energy cost represent the cost

computed based on dynamically simulated

behaviour profiles. The difference between the

theoretical and actual energy cost gives energy

performance. This function outputs complete

behaviour profile generated dynamically and its

associated power management solution.

4 SCENARIOS’ DESCRIPTION

We have collected a workday activity profile (24h)

of a family in France through an activity journal

(

Fig.7) with contextual information.

Figure7: Activity journal for data collection.

To demonstrate reactive/deliberative group behaviour

a simple scenario from the collected profile is

implemented using the Brahms language following

the model proposed in section 3:

“Stephan (father) comes back home from LAB at

19h48 and walks through the corridor to the kitchen

for dinner. Anna (daughter) and Erik (son) are

watching television in the lounge. They walk to the

kitchen for dinner at 19h50. Katherine (mother) is

already in the kitchen and is preparing the table for

dinner and is interacting with the fridge in parallel.

Stephan drinks water from the refrigerator. They

have dinner together from 19h50 till 20h30. Stephan,

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Katherine and Anna move to the living room after

finishing the dinner and start watching television

there. Erik moves to the study room.

The temperature increases slowly due to the

presence of all family members in the living room.

Stephan feels hot and wishes to open the window to

reduce the temperature. Before opening the window

he asks Katherine and Anna. They agree and Stephan

goes to the window to open it. He realises that there

is a storm outside and opening window is not safe, so

he evaluates between two options to identify the most

comfortable (i) turn the AC on using the remote

control, (ii) open the door which is linked to the study

room. He decides to turn on the AC as opening door

might disturb Erik“.

To implement the above scenario we need to model

human cognition, reactive/deliberative behaviour

(group agreement), context (5W1H) and dynamism

(temperature increase slowly). Results from the

simulation represent human behaviour at multiple

levels of detail and interactions between agents and

objects. Sub-functions “A1 to A9” (Fig.6) are

implemented and simulated in Brahms with results

in section 5. Sub-function “A9” will be deve loped

as a plug-in to be integrated with the Brahms

simulation for energy calculation.

5 SIMULATION RESULTS

The scenario in section 4 is implemented and

simulated using the model (section 3) with the

Brahms language. It starts with Brahms code (using

composer), compiled (using builder) to create ‘.xml’

files and simulation results are generated as a text file

using the Brahms simulation engine. The simulation

text file is converted into a MySQL database by agent

viewer in order to graphically analyze the simulation

results as presented below in

Fig.8. Only a part of the

simulation results is shown here that takes into

account the reactive, deliberative and group behavi-



Figure 8: Communication and Group reactive/deliberative behavior.

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our. Communication activities taking place between

agents Stephan, Anna and Katherine are represented

by vertical lines and the bulb represents the Brahms

throughtframe (tf).

It is evident from the Fig. 8 that agents Stephane,

Katherine and Anna have moved to the living room

after having dinner in the kitchen. The first

throughtframe with reference to the agent Stephane

highlights the temperature increase in living room

beyond 30 degrees and Stephane feels hot and want

to lower the temperature. In order to establish

dynamic group behaviour in the presence of

Katherine and Anna, Stephane decides to go for

group agreement and to establish this he starts

communication with other agents.

This belief gives rise to the deliberative

behaviour and now he wants to choose between

opening the door or turning on the air conditioner

based on Erik’s presence in the study room.

However, changing the parameter with no storm

outside will not trigger the thoughtframes used for

further reasoning and the simulation results will be

different. Horizontal lines beneath the primitive

activities (pa) show the interaction with some

appliance/object.

6 CONCLUSIONS

From the results, we have demonstrated the

simulation of reactive/deliberative group behaviour

within domestic settings (complex scenario).

Perception, cognitive, social and psychological

elements are dynamically simulated to generate

behaviour of inhabitants’ over time. We are working

on a Java plug-in to connect to the behavioural

pattern generated from Brahms for energy

calculations and learning context, beliefs and facts

that influence energy consumption within the

domestic environment. We are also working on the

sub-function “A9” to calculate energy related to the

dynamic behaviour profiles generated from

simulation and build a database (behaviour base) of

context, beliefs, facts and activities having strong

influence on the energy consumption. During

simulation, agents are provided with potential

consequences of possible actions learned from

previous simulations in anticipation to find energy

efficient behaviour and savings.

7 FUTURE WORK

In this article dynamic behaviour is demonstrated

with data collected from a single family, however

future work will include data collected from a set of

reference households. Dynamic behaviour

simulation could be extended to model patterns for

different classes of household behaviours and

analysis of energy impact due to correct behaviour

(ergonomy). Simulation results as presented in

section 5 start with the initialization of beliefs and

facts as static values; hence based on fixed initial

values we could have one behavioural pattern;

however adjusting the list of beliefs and facts

dynamically after each simulation within parametric

space could be interesting to identify generalized

energy related behaviour. Design of experiment

(DOE) and data mining techniques if employed

would help to reduce the number of possible

combination of facts and beliefs to start simulations

and optimize the computational time in terms of

reduced number of experiments.

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