Capturing Context Information in a Context Aware Virtual Environment

Helio H. L. C. Monte-Alto and Elisa H. M. Huzita

State University of Maring

a, Informatics Department, Maring

a, Paran

a, Brazil

Keywords:

Virtual Environment, Context Awareness, Collaborative Work, Multi-agent Systems.

Abstract:

Designing context aware applications is a great challenge given the complexity of such systems, specially

concerning the mechanisms to provide sensing, or capturing, of context information. There are many works

in literature toward providing context awareness in physical environments, such as pervasive systems. How-

ever, when dealing with virtual environments, such as distributed environments to support collaboration for

teams distributed geographically, an increase on complexity of its design comes up. There is a need to model

a software system which uses concepts present in physical environments in order to try to reduce the disad-

vantages of the distribution. This work explores this challenge, focusing on conceiving a solution for context

awareness in distributed virtual environments. We also present a model for designing a platform to support the

development of multi-agent based context aware virtual environments.

1 INTRODUCTION

Context awareness has been extensively used in dis-

tributed systems, specially pervasive systems to pro-

vide smart physical spaces. A context aware en-

vironment should be able to allow perception of it

and autonomously execute actions in order to opti-

mize its operations, its behaviours and its interactions

with the user (Viterbo et al., 2009). However, there

are other applications of context awareness beyond

physical environments. There is a growing need for

virtual environments, specially to support collabora-

tive work for users geographically distant from each

other. In such environments, context perception is not

as straight forward as in physical environments, in

which context is captured through physical sensors.

In distributed and virtual environments, such percep-

tion must be done exclusively by software entities, re-

quiring an appropriate software architecture to sup-

port context awareness.

In order to implement context aware systems, one

of the most suitable approaches is by means of multi-

agent systems, given that an agent is a computational

entity which is autonomous, situated and able to sense

in an environment (Wooldridge, 2002). Agents also

may interact with other agents, making it appropriate

for collaborative environments.

Once understood such ideas for context aware-

ness, it has been proposed a multi-agent infrastructure

to support the development of context aware systems

for virtual environments called CAKMAS (Context

Awareness and Knowledge-Based Multi-Agent Sys-

tems) (Monte-Alto et al., 2013). In its current archi-

tecture, it proposes solutions for persistence and dis-

semination, although its aim is to provide much wider

support for context awareness. One of the key is-

sues to be addressed in context aware environments

is the need of capturing context information. This

paper presents a solution for this problem, extending

CAKMAS, and focusing on mapping the concepts of

a physical environment and using them to design a

virtual multi-agent environment where agents are able

to sense what is happening in order to take decisions

and react accordingly.

This paper is divided in six more sections. Sec-

tion 2 presents some works towards context aware

and multi-agent architectures and their ﬂaws regard-

ing context capturing in virtual environments. Sec-

tion 3 presents the conceptualization to help ﬁguring

out how should a virtual environment be designed in

order to properly support context awareness. Section

4 presents our proposal for a multi-agent platform to

support virtual environment with context awareness

capabilities. In Section 5 it is presented a case study

to demonstrate the functionality and effectiveness of

the proposed architecture. Finally, conclusions and

future works are discussed.

605

H. L. C. Monte-Alto H. and H. M. Huzita E..

Capturing Context Information in a Context Aware Virtual Environment.

DOI: 10.5220/0004885406050612

In Proceedings of the 16th International Conference on Enterprise Information Systems (ICEIS-2014), pages 605-612

ISBN: 978-989-758-028-4

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

2 RELATED WORKS

One of the most adopted approaches for context aware

systems is the middleware infrastructure, which in-

troduces a layered architecture for context aware sys-

tems with the intention of hiding low-level sensing

details (Chen, 2004; Baldauf et al., 2007). In order to

provide middleware solutions, it has been proposed

agent-based systems as the approach for implement-

ing these systems. Such choice gained popularity due

to agent’s characteristics, which brings decentraliza-

tion and facilitates reasoning and sharing of context

information.

A multi-agent system requires that the agents have

an environment in which they live, act and inter-

act. Many context aware multi-agent systems archi-

tectures have been proposed in the literature thus far.

However, most works focus on physical environments

instead of virtual environments, and how to provide

context awareness in pervasive systems. There is a

lack of works focused on exploiting mechanisms to

allow software agents to sense what is happening in

a virtual environment, as well as more intuitively de-

signing virtual environments based on the concepts of

physical environments, as presented in Section 3.

The ACAI Framework, presented by Khedr and

Karmouch (2005), proposes agents that accomplish

several requirements of context awareness, such as

context composition, inferring, and inter-domain con-

text invocation. This project focuses on issues of con-

text representation and reasoning in agent systems for

pervasive environments.

CoBrA (Chen, 2004) is an agent-based architec-

ture to support context aware system in smart spaces -

i.e. physical and pervasive environments. Its architec-

ture focuses on a special agent called Context Broker,

which maintains a shared model of the context of the

environment, providing it for a community of agents,

services and devices. As it focuses on capturing con-

text from physical sensors, there is no concern about

sensing context in virtual environments.

CAKMAS is an architecture whose purpose is to

support knowledge-based and context-aware multi-

agent systems, specially for collaborative virtual en-

vironments. It is based on the DiSEN-CSE model,

which deﬁnes a whole cycle for context-awareness,

including capturing, processing, disseminating and

persisting context information. The current status of

this project focuses on persistence and dissemination

of context. This paper focuses on extending CAK-

MAS to support context capturing / sensing properly.

There are also many works focused on virtual en-

vironments in the area of Virtual Reality. However,

they usually deal with immersive digital environments

or virtual spaces, i.e., 3D environments which try to

simulate physical environments (Churchill and Snow-

don, 1998). Such works are concerned with issues re-

lated with sensing similar to physical environments,

such as distance and collision of virtual objects. This

is not the aim of our work, which proposes a dis-

tributed software system which inherits some charac-

teristics of physical environments in order to improve

awareness and collaboration for a distributed team.

Further detail will be covered in Section 3.

3 CONCEPTUALIZATION

3.1 Designing a Virtual Multi-agent

Environment

The term ”Virtual Environment” is increasingly being

part of the life of most people. Everyone is connected,

and the Web has become an extension of the phys-

ical environment for many. Although the term Vir-

tual Environment is often used for any software sys-

tem in which the user interacts, there are some more

speciﬁc deﬁnitions, specially in the area of Virtual

Reality (VR). According to Churchill and Snowdon

(1998), a Collaborative Virtual Environment (CVE)

is a computer-based, distributed, virtual space or set

of places in which people can meet and interact with

others, with agents or with virtual objects.

The idea of virtual environments are very

widespread in the area of collaborative working as a

way of supporting awareness, coordination and com-

munication in a distributed team (Benford et al., 2001;

Churchill and Snowdon, 1998). Therefore, such con-

cept is very important for this work since it deﬁnes

the way the users will interact to it and also with other

users through the environment.

In order to achieve a suitable virtual environment

to support collaboration in a distributed team, there is

nothing better than basing it on concepts involved in a

physical environment. Although our aim is not to con-

ceive a virtual environment which totally simulates a

physical environment, we can take some characteris-

tics and concepts from real physical environment in

order to design a suitable virtual environment for col-

laboration, as it is going to be exposed as follows.

3.2 Differences Between Physical and

Virtual Distributed Environments

In order to capture good ideas regarding how to de-

sign a virtual environment, it is interesting to try to

highlight differences between a physical environment

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and an hypothetical virtual environment. To accom-

plish this, it is necessary to take an overview of both

environments, as it is exposed on Figures 1 and 2.

Figure 1: Physical environment.

In physical environment, an individual (or agent)

interacts with physical objects and resources that are

present in the environment, and also interacts with

other individuals. All these interactions occurring

in the environment are naturally and cognitively per-

ceived by other individuals in the environment, based

on their interest and proximity in the event. In sum-

mary, it is possible to highlight three main character-

istics of such environment:

1. An agent acts in the environment by interacting

with its objects and resources. It does that by

means of actuators provided by the environment

and linked to these objects and resources. Exam-

ple: an agent is able to open a door because it has

a knob which allows such action.

2. The agent is also able to interact (or socialize)

with other agents in the environment by some

means of communication, such as spoken conver-

sation and gestures.

3. The agents can perceive what other agents are

doing in the environment. This usually happens

when they are interested in what the other agent

is doing, or what is going on with it. The interest

may be focused on the agent in particular, in the

action the agent is taking, or both. In other terms,

we can say that an agent intercepts the interactions

of one or more different agents in order to gather

useful information earlier. This also characterize

the concept of awareness.

Summarizing, we have three main characteristics

of an environment: (i) action, (ii) socialization and

(iii) awareness. The great challenge is how to design

and implement such concepts in a distributed and vir-

tual environment. Figure 2 shows an overview of a

distributed environment and its concerns about these

issues.

One very important concept that arises from all

this scenario is context. An agent needs to be aware

of the context of the environment in order to facilitate

seeking its goals. The way the context is captured in

a physical environment is natural and sensed by hu-

man cognition. As well as a human agent is capable

of hearing and seeing what interesting events are hap-

pening around him/her, software agents must be able

to capture interesting information from the environ-

ment. They must also be capable of processing the

information captured from the environment in order

to turn it in reasonable information - or knowledge

- because conclusions or new information can be in-

ferred from it.

This work is specially concerned in capturing

context information from the environment, providing

proper awareness for the agents in a distributed and

virtual environment.

4 CAKMAS PLATFORM

4.1 Overview

Based on CAKMAS architecture (Monte-Alto et al.,

2013), we propose a FIPA-compliant Agent Platform

(AP) (FIPA, 2000), extending CAKMAS toward a

broader context aware supporting, taking into consid-

eration all the conceptualization presented in Section

3. Such platform architecture is going to be called

CAKMAS Platform. In this paper, we will focus on

the ideas and mechanisms to support awareness in

such platform. In other words, we present solutions

for the problem of agents perceiving events in the en-

vironment according to their interest.

An overview of CAKMAS Platform can be seen

at Figure 3. It illustrates the agents involved in the

environment, including specialized agents of the plat-

form, responsible for managing the platform and pro-

viding context awareness facilities. The platform is

composed of the following specialized agents:

• Manager Agent (MA): a FIPA based agent that

exerts supervisory control over access to the AP.

It also maintains a directory of registered agents

identiﬁers. Each agent must register with the MA.

• Directory Agent (DA): another FIPA special agent

that provides yellow pages services for the agents.

Environment services must be registered in this

agent in order to provide description and discov-

ery for them. Agents also may register their ser-

vices with the DA or query the DA to ﬁnd out what

CapturingContextInformationinaContextAwareVirtualEnvironment

607

Figure 2: Virtual environment.

services are offered by other agents.

• Service Broker Agent (SBA): Inspired by a simi-

lar solution proposed by Tapia et al. (2008), this

agent is responsible for managing incoming re-

quests from the user to be processed by services.

It basically invokes the service and returns the

response back to the users, ensuring that every

action from the user in the environment passes

through the AP, which allows context capturing

by the agents.

• Ontology Agent (OA): the agent responsible for

persisting and disseminating context information

among the agents in the environment (Monte-Alto

et al., 2013).

The remaining agents are application speciﬁc

agents. Every agent in the platform is called a CAK-

MAS Agent, and inherits some special features that

must be available in order to support context aware-

ness, as explained below.

4.2 Agent Internal Architecture

The internal architecture of a CAKMAS Agent is

shown on Figure 4. Such architecture is character-

ized by the BDI (Belief-Desires-Intentions) model

and four stages which deﬁne its behaviour and life cy-

cle. This four-stages behaviour is compliant to the ar-

chitecture of SemantiCore, an AP for Semantic Web

applications (Blois et al., 2007). It also matches the

essence of agents, which must sense the environment

by sensors, make some decisions based on its knowl-

edge and act accordingly through actuators (Russell

and Norvig, 2003).

The belief of an agent in CAKMAS is repre-

sented as knowledge about the environment - includ-

ing the context of the environment - and related ap-

plication domain, as well as the rules involved. Such

knowledge is described and implemented by means

of ontologies and the rules are implemented as infer-

ence rules, using specially Web Semantic standards

and technologies such as OWL, RDF, SPARQL and

SWRL (W3C, 2001). It is also by means of ontologies

that context information (or context knowledge, given

that the context is represented semantically) is rep-

resented in our architecture, as explained in (Monte-

Alto et al., 2013). This provides a uniformity in the

way context knowledge is shared and used for reason-

ing.

The desires of an agent are the goals it wants to

achieve. A goal can be deﬁned as a state, or a set of

facts that the agent wants to be true in the environ-

ment. An agent knows when a goal is achieved when

its knowledge about the environment matches the de-

scription of the goal. When the agent decides, based

on its goals, to commit with an action plan, an inten-

tion is created. In other terms, the intentions of an

agent are comprised of action plans that it decided to

take in order to reach its goals.

Goals also deﬁne the interests of an agent. An

agent may be interested only on information that can

help it to reach its goals, and such information is cap-

tured by the agent by means of its sensors. This way,

the desire of an agent deﬁnes which kind of context

information it wants to be aware of.

The four-stages model of the agent behaviour is

also totally focused on the BDI model. A sensor cap-

tures messages from the environment and ﬁlters them

according to the interest, deﬁned based on the desires,

of the agent.

The decisory component is the responsible for

reasoning about the facts coming from the environ-

ment over its own knowledge (belief), aiming to

achieve its goals. In general, such decisions are made

by means of running an inference engine with infer-

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Figure 3: CAKMAS Platform.

Figure 4: Internal Architecture of a CAKMAS Agent.

ences rules over the current knowledge the agent has

about the context of the environment. These decisions

should result in the execution of actions to react ac-

cordingly to these events and also pro-actively try to

achieve its goals by acting in the environment and in-

teracting with other agents collaboratively.

The execution component use the concept of

workﬂow to execute actions in speciﬁc orders based

on the decisions taken by the decisory component.

The actions can also use speciﬁc actuators to act in

the environment - e.g. the service to send an e-mail re-

quires a speciﬁc actuator to invoke this service. Given

that many agents may have to execute similar actions

and use the same actuators, such architecture allows

reusing of actions and actuators.

Regarding context capturing support, CAKMAS

Agents use the pattern Observer to get informed about

events which they may be interested. Each agent has a

list of subscribers, which are the agents that are inter-

ested in the actions of this agent. Whenever the agent

acts in the environment, usually by means of message

exchanges, each one of its subscribers also receives

the message. In other words, the subscribers intercept

the message of the publisher, becoming aware about

the occurrence.

4.3 Service Broker Agent

The Service Broker Agent (SBA) is responsible for

providing an interface between the agents of the plat-

form - usually User Agents - and the services pro-

vided in the environment by managing service re-

quests and responses. It was based on the FUSION@

architecture proposed by Tapia et al. (2008), and its

functionality is detailed in Figure 5 and described as

follows:

1. An agent sends to SBA a request for a service

available in the environment, probably discovered

via DA;

2. SBA receives the request via its sensor and pass

the message to its decisory component;

3. The decisory component makes some security

checks by asking DA whether the service really

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Figure 5: Service Broker Agent behaviour.

exists and if its request message is semantically

and syntactically correct.

4. Following the decision to continue the process,

some actuators are called. One of them invokes

the service properly. The other sends the informa-

tion about the service request to every subscriber

of SBA, which is the key to provide awareness in

the environment.

5. SBA receives a response from the service invoked

via an appropriate sensor, and send it to the de-

cisory component;

6. The service response is identiﬁed and packed in

order to send the response to the requester. It also

deregisters the service as being done.

7. The agent sends the response for the requester

agent and also for every subscriber.

Given this process, we have a combination of

the Observer approach and the broker architectural

model to provide awareness for the agents in the vir-

tual environment.

5 EMPIRICAL STUDY

In complex and distributed systems it is often not

straight forward to ﬁgure out every detail of its infras-

tructure. CAKMAS Platform provides such kind of

infrastructure, thus it is expected that uncertainty dur-

ing the modelling of such architecture comes up. It is

necessary some empirical study to ensure that every

detail has been covered and make sure that it is effec-

tive. To accomplish this, a case study was carried on,

aiming to give strength to our proposal and also ex-

panding the sight to a particular application. A proto-

type of the scenario was also implemented in order to

allow an experimental study and to catch a glimpse of

the improvements introduced by CAKMAS Platform.

The scenario used for this study is the same appli-

cation which is the focus of research of our research

group: a Global Software Engineering environment,

called DiSEN. Part of its architecture aims to provide

context awareness to such environment by means of

some components such as the Middleware, the Agent

Manager and the Object / Service Manager.

The idea of this case study is to map DiSEN’s

components to CAKMAS Platform’s elements, as if

it was the base for DiSEN’s context awareness fea-

tures’ implementation. The scenario, already present-

ing such mapping, is represented in Figure 6, and de-

scribed as follows.

5.1 Preconditions

Developer Bob is member of a team which is work-

ing on project DiZEN (a ﬁctional and contradictory

project) and whose project manager is Alice. Bob has

been allocated to work on a task T with a certain pri-

ority P1. Therefore, his personal agent BobAgent is

interested in being aware when there is a change in

T’s priority. Alice also has a personal agent called

AliceAgent, which may act in the environment on her

behalf.

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Figure 6: DiSEN with CAKMAS scenario.

5.2 Description

The following is the exact description of the interac-

tions that take place in our scenario:

1. Alice, the project manager, interacts with the en-

vironment and asks its personal agent AliceAgent

to change task T priority;

2. AliceAgent receives this request and looks for the

adequate service.

3. AliceAgent creates a service request following a

speciﬁed standard (e.g. SOAP) and sends it to

SBA;

4. SBA takes the required actions as described in

Section 4.3, and then invokes the service re-

quested. It also publishes the request to all sub-

scribers, which includes BobAgent;

5. BobAgent receives the message containing the

service request from AliceAgent, becoming aware

of Alice’s wish to change T priority. It knows that

the message is interesting based on its interest, or

desire, as explained in Section 4.2;

6. BobAgent notiﬁes developer Bob about the prior-

ity change properly. Such action - the notiﬁcation

- is triggered when its decisory component reason

about the fact that one of the tasks Bob is working

had its priority changed;

7. While Bob becomes aware of Alice’s action, the

request is processed by the service provider, i.e.,

the service implementation. The context knowl-

edge is also generated and sent to the OA, which

will check consistency, persist and disseminate it.

In case of success, SBA can conﬁrm the action

to BobAgent. Such conﬁrmation may also end up

being made by the OA, which will disseminate the

new context information later. Otherwise, SBA

can inform BobAgent that the operation triggered

by AliceAgent was not successful and advice Bob

properly about it.

5.3 Post-condition

The project manager Alice made his priority change

of T successfully and the developer Bob could be

aware of this context change immediately.

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5.4 Implementation and Experimental

Study

The given scenario, as well as a preliminary version

of CAKMAS Platform, was implemented in order to

foresee the improvements of using the proposed ap-

proaches for context capturing. As a continuation of

the previous CAKMAS implementation, it was im-

plemented in Java, based on the framework Semanti-

Core, using Jena to handle ontologies.

The scenario was run with and without the mech-

anism introduced in CAKMAS Platform. In the case

without the SBA, BobAgent gets informed when the

OA disseminates the new context in the environment,

while it persists it in the knowledge base. Each case

was run 40 times to take averages. The detailed ex-

perimental results and analysis are out of scope of

this paper given the paper length limitation. However,

just to give a glimpse of the results, the average time

BobAgent took to be aware of Alice’s action without

the SBA was 636,03 milliseconds, whereas the same

with SBA was 58,29 milliseconds. The improvement

in the time to be aware of context is signiﬁcant for the

scenario, and we can deduce that it must reﬂect in a

real environment.

6 CONCLUSIONS AND FUTURE

WORKS

Proposing solutions for context aware virtual environ-

ments is justiﬁed by the increasing demand for tools

to support collaboration among people geographically

distributed. In such cases, the lack of a common en-

vironment in which people can act, interact and be-

come aware of the situation demands some kind of

software system to improve coordination and aware-

ness. CAKMAS Platform is proposed as an exten-

sion of CAKMAS architecture taking into account the

concept of environments, agent platforms and context

capturing. As presented in Monte-Alto et al. (2013),

previous work has focused on reasoning, persistence

and dissemination of context information, but lacked

proper ways of capturing it.

The proposed solution was analysed and validated

by means of a case study and an experimental study

using a prototype. Such empirical studies allowed

us to ﬁgure out in more detail the functionality and

effectiveness of CAKMAS Platform. However, fur-

ther validation including a more robust implemen-

tation and experimental study is necessary to give

strength to our proposal, which will be covered in fu-

ture works.

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