

Knowledge modeling through computational agents: application to surveillance systems


Knowledge modeling through computational
agents: application to surveillance systems

José M. Gascueña,1 Antonio Fernández-Caballero,1

Marı́a T. López,1 and Ana E. Delgado2

(1) Universidad de Castilla-La Mancha, Departamento de Sistemas Informáticos & Instituto
de Investigación en Informática de Albacete, 02071 Albacete, Spain
(2) Universidad Nacional de Educación a Distancia, Departamento de Inteligencia Artificial,
E.T.S.I. Informática, 28040 Madrid, Spain

Abstract: In this work the concept of computational agent is located within the methodological framework of
levels and domains of description of a calculus in the context of different usual paradigms in Artificial
Intelligence (symbolic, situated, connectionist, and hybrid). Emphasis in the computable aspects of agent
theory is put, leaving open the possibility to the incorporation of other aspects that are still pure cognitive

nomenclature without any computational counterpart of equivalent semantic richness. The ideas presented are
shown as currently being implemented on semi-automatic surveillance systems. A case study of a mobile robot
application for the detection and following of humans is described.

Keywords: artificial intelligence, computational agents, knowledge engineering, surveillance

1. Introduction

The concept of agent comes from the persistent

attempt in Science and Engineering to modular-

ize the knowledge necessary to specify a calcu-

lus, and of the later attempt to progressively

increase the level of complexity and autonomy,

making it reusable (Sycara et al., 1996). Agent

theory takes from Artificial Intelligence (AI) the

general intention to approach the functionality

of biological systems. Thus, there are adaptive,

intelligent, and intentional agents, with learning

capacity and equipped with a certain level of

social organization that allows cooperation in

the accomplishment of ‘group tasks’. In this

sense, the objectives of multi-agent systems

(MAS) and distributed AI (DAI) agree (Weiss,

1999). ‘Nature-inspired computation’ (Nunes,

2006) is also practically isomorphic to agent

and MAS theory. This is true at the level of

individual agents (‘organisms’) as well as at the

level of social organizations in MAS (ants, bees,

human societies, collective games, and so on).

In this last case, to the functionalities demanded

for the individual behavior, it is necessary to add

an interaction language among agents that al-

lows to share goals and to coordinate collective

plans used to reach the goals.

With the arrival of AI, the initial formulations

of connectionism (artificial neuronal networks

and modular theory of deterministic and prob-

abilistic automata) are partially obscured by

new symbolic formulations based in rules. Here

the inference is understood as a search process in

a states space. Thus, the idea of ‘actors’ appears

as a concurrent computation model in distribu-

ted systems (Agha, 1986), which along with

object-oriented programming (Meyer, 1997) are

the antecedents of the agents. Conceptually, it is

Minsky (1961) who raises the social idea of

DOI: 10.1111/j.1468-0394.2011.00609.x

Article _____________________________

306 Expert Systems, September 2011, Vol. 28, No. 4 c� 2011 Blackwell Publishing Ltd


agency, essentially basing on ‘personification’ of

the verbs used in natural language to describe

the necessary processes for the execution of a

certain activity. The strategy to describe in

natural language an agent’s ‘beliefs’, ‘desires’

and ‘intentions’, and later to develop the formal

counterpart of the linguistic terms, is still used at

the present time (Russell & Norvig, 2002).

In fact, a complete agent language is dominant

in Software Engineering (SE) (Padgham &

Zambonelli, 2006), and its importance also grows

in Knowledge Engineering (Cuena et al., 2003)

For instance, an agent-based framework is a

plausible solution to achieve inter-operability

between domain ontologies used by different

knowledge-based systems (Orgun et al., 2008; Li

& Yang, 2008). It is advisable to indicate that, as

with the term of AI, in the agents field there is

usually an abuse of excessively loaded cognitive

nomenclature of anthropomorphous semantics.

Finally, it is during implementation when algo-

rithms and automata are translated into sentences

written in an existing programming language.

In this work we approach the general concept

of agent from a computational perspective. We

consider that an agent starts being a conceptual

model, later it is reduced to a formal model, and

inally to a machine with sensors, effectors and a

control program. The rest of the work is struc-

tured as follows. Section 2 introduces some

methodologies and programming languages for

computational agents. In section 3 the agent

concept is located within the methodological

framework of description levels and domains of

a calculus. In sections 3.1 and 3.2, respectively,

our vision of a computational agent’s concep-

tual and formal model is explained. Finally

sections 4 and 5 introduce the application of

our ideas in the surveillance domain.

2. Some methodologies and programming

languages for computational agents

The emergence of a new paradigm in Computer

Science involves establishing a solid theoretical

base and the software tools necessary to demon-

strate its viability. In agent-oriented soft-

ware engineering (AOSE), different approaches

about development methodologies and pro-

gramming languages have evolved in parallel.

First, a methodology needs to have defined the

set of concepts used, a process that specifies

what to do, the collection of models obtained,

and the notation used to represent them (Bordi-

ni et al., 2007). In the last decade, various

research groups have proposed their own meth-

odology to develop applications based on agents

(Bergenti et al., 2004). Many proposals can be

found in the literature to evaluate them. For

example, Cernuzzi et al. (2005) analyze the

process model (waterfall, evolutionary, incre-

mental, spiral, transformations) and the phases

covered in the methodologies. Sturm and

Shehory (2004) analyze the concepts and prop-

erties, the notations and modeling techniques,

and the development processes used. The con-

clusion reached is that definitely there is no

methodology better than the others. Table 1

briefly summarizes the analysis of methodolo-

gies ADELFE (Carole et al., 2003), PASSI

(Cossentino, 2005), Prometheus (Padgham &

Winikoff, 2004), INGENIAS (Pavón et al.,

2005), Tropos (Bresciani et al., 2004) and O-

MaSE (Garcı́a-Ojeda et al., 2008) according to

the following topics:

� Is it a general purpose methodology or on
the contrary is it intended to apply to a

particular type of system?

� Does it reference any agent architecture in
particular? The answer to this question is to

find out if there is an agent architecture

linked to the methodology or conversely

whether the methodology gives the designer

freedom to choose what he=she wants and
even to create his=her own.

� Are there guidelines provided for identifying
agents? That is, does the development process

offer some preliminary steps that lead to

determine the types of agents in the system?

This approach is particularly interesting be-

cause in AOSE methodologies agents are

regarded as first-order entities and therefore it

is helpful to have guidelines to identify them.

� Is there a tool that gives support? The avail-
ability of a tool will offer the designer a

c� 2011 Blackwell Publishing Ltd Expert Systems, September 2011, Vol. 28, No. 4 307


support necessary to create the models used

in the methodology.

� Does the tool provide support to follow the
development process? That is to say, does

the graphical interface refer to the develop-

ment process?

� Does the tool generate code in some agent
language?

� Does the tool provide utilities so that the
user may add a new application that gener-

ates code in the desired language?

Moreover, the objective of agent programming

languages and platforms is to facilitate the

implementation of systems incorporating

agent concepts (Unland et al., 2005). Table 2

shows some of the features present in languages

JACK (Winikoff, 2005), JADE (Bellifemine

et al., 2007), Jadex (Pokahr et al., 2005) and

ICARO-T platform (Garijo et al., 2008): (1) the

type of agents that can be implemented, (2) the

language used, (3) how it performs reasoning,

(4) whether or not the architecture is compliant

to the FIPA standard, (5) whether or not there is

a specific development environment, and (6) if it

is open source or not.

As shown in Table 1, most methodologies can

be applied in developing any general purpose

MAS. However, this is relatively true as they

offer a final phase that includes models that are

closely tied to specific agent architecture. There-

fore, we believe that to develop an application,

and in order to identify the types of agents and

their interactions, the starting stages should be

independent of the target architecture. Once you

have reached this milestone, appropriate models

are used according to the internal architecture

of each agent under consideration. Lastly, it is

Table 1: Agent-oriented software engineering methodologies

ADELFE PASSI Prometheus INGENIAS Tropos O-MaSE

Specific purpose Yes (Adaptive
MAS)

No No No No No

Specific agent
architecture

Cooperative
agents

FIPA agents BDI agents Based in
mental agents

BDI agents No

Guide for
identifying agents

Yes Yes Yes No Yes Yes

Graphical support
tool

OpenTool
(commercial)

PTK (add-in
for Rational
Rose)

PDT IDK TAOM4E AgentToolIII
(aT3)

Assistance for
following the
development
process

Yes No Yes No Yes Yes

Code generation No JADE Jack JADE JADE,
Jadex

JADE

Utility for creating
new generators

No No NO Yes (modules) No No

Table 2: Agent programming languages

JACK JADE Jadex ICARO-T

Agents BDI – BDI Reactive and cognitive
Language Java extension Java Java Java
Reasoning BDI No BDI Automata and cognitive agents
FIPA compliant NO Yes Yes No
Development environment JDE No No No
Open source No Yes Yes Yes

308 Expert Systems, September 2011, Vol. 28, No. 4 c� 2011 Blackwell Publishing Ltd


possible to generate code for the language cho-

sen for the implementation. It is also note-

worthy that most methodologies consider the

code generation phase for a given agent pro-

gramming language. In ADELFE this defi-

ciency has been corrected in version 2.0

(Rougemaille, 2008). Equally important is the

possibility offered by INGENIAS, compared to

other methodologies, to create new modules

through a mechanism based on templates to

generate code in the desired target language.

The O-MaSE methodology does not assume

any particular agents; however, the interaction

protocols model conversations between two

agents. So, it is difficult to understand how

interactions take place at the system’s global

level. Finally, notice that the special-purpose

methodologies such as ADELFE should raise

questions to allow the designer to determine if

an application is a good candidate to be mod-

eled like the specific MAS considered in the

methodology.

Regarding the languages discussed, notice that

all of them facilitate the implementation of a type

of agent, except JADE that is only a middle ware

that facilitates communication between agents. In

JACK a pre-compiler that takes as input files

written in JACK language and gets as output

Java code is needed. This code is finally used to

compile and run the application. By contrast, in

other languages shown in Table 2 this utility is not

necessary, as programming is directly done in

Java. JACK programs are developed using a

specific development environment (JACK Devel-

opment Environment or JDE).

Here are specific details of the Prometheus

methodology and the ICARO-T platform=frame-
work. Prometheus is defined as a proper detailed

process to specify, implement and test=debug
agent-oriented software systems. It offers a set of

detailed guidelines that includes examples and

heuristics, which provides a better understanding

of what is required in each step of the develop-

ment. This process incorporates three phases:

� The System Specification phase identifies the
basic goals and functionalities of the system,

develops the use case scenarios that illustrate

its functioning, and specifies which are the

inputs (percepts) and outputs (actions).

� The Architectural Design phase uses the
outputs produced in the previous phase to

determine the agent types that exist in the

system and how they interact.

� The Detailed Design phase focuses on devel-
oping the internal structure of each agent

and how each agent will perform its tasks

within the global system. Finally, Pro-

metheus details how the entities obtained

during the design are transformed into the

concepts used in a specific agent-oriented

programming language (JACK). The design

process for Prometheus methodology is sup-

ported by Prometheus Design Tool (PDT)

(Padgham et al., 2008).

ICARO-T (Garijo et al., 2008) is an open source

framework (http://icaro.morfeo-project.org)

that provides four categories of component

patterns: agent organization pattern to describe

the overall architecture of the system, cognitive

and reactive agent patterns to model agent

behavior, and resource patterns to encapsulate

computing entities providing services to agents.

More basic computing entities including com-

ponents for building new agent and resource

models are also available in the frame-

work. Examples of these entities are abstract

data types, specialized libraries, domain ontolo-

gies, rule processors, buffers, and so on. The

ICARO-T reactive agent conceptual architec-

ture (Gascueña et al., 2010) is made up of three

components: perception, control and actuation.

The perception works as an event handling

mechanism. It stores incoming events, delivering

them to the control on demand. The control is

modeled as an extended finite state machine that

consumes events stored in the perception, and

performs transitions by changing its internal

state and invoking actions in the actuation

model. Reactive agents behave like event-con-

suming processes which change their internal

state and execute operations according to their

state transition table.

The main advantage of the ICARO-T frame-

work is that it provides to engineers not only

c� 2011 Blackwell Publishing Ltd Expert Systems, September 2011, Vol. 28, No. 4 309

http://icaro.morfeo-project.org
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concepts and models, but also customizable

MAS design and java code fully compatible with

SE standards, which can be integrated in the

most popular IDEs. While other agent based

platforms (Unland et al., 2005), such as FIPA,

focus on communication standards, ICARO-T

focus on providing high level software compo-

nents for easy development of complex agent

behavior, agent coordination, and MAS organiza-

tion. An additional advantage for ICARO-T is

cost-effectiveness of agent patterns in application

development. Evaluation results in previous ex-

periences (Garijo et al., 2004) have reported

significant reductions by an average of a 65% of

time and effort in the design and implementation

phases. Cost reduction was achieved without

minimizing or skipping activities like design, doc-

umentation and testing. In the phases of testing

and correction the errors were also reduced.

3. Levels and domains in computational agent

models

Since the introduction of the knowledge level by

Newell and Simon (1972) and Marr (1982) –

called ‘theory of calculus’ – it is usual to describe

the knowledge necessary to understand any

calculation in three levels: physical (PL), sym-

bols (SL) and knowledge (KL). Or, in a simpler

way: machine hardware, programs and models,

and algorithms. Starting from the idea of refer-

ence systems in Physics and the proposals by

Maturana (1999) and Varela (1979) in Biology,

Mira and Delgado (1987) introduces the figure

of the external observer in computation. This

gives rise to two description domains of the

organizations and relations at each level: (1) the

own domain of the level (OD), where the caus-

ality is intrinsic and the semantics comes im-

posed by the structure and dynamics of the level,

and, (2) the external observer domain (EOD) to

the computation carried out in that level, where

the semantics is arbitrary and the interpretation

of the calculation depends of the observer and,

in general, of the application domain.

When superposing both domains (OD, EOD),

at the three levels (KL, SL, PL), we obtain a

three-storey house and six apartments (two by

floor), in which the agents reside. Thus, in this

framework, a user has available six types of

knowledge – (PL-OD), (PL-EOD), (SL-OD),

(SL-EOD), (KL-OD) and (KL-EOD), respec-

tively – to describe agents. The floor where an

agent resides suggests the agent name. So, we

can state that there are three types of agents

(physical agent, symbolic agent, and knowledge

agent), which can be described from a point of

view related to observer domain and another

one related to external observer domain.

The case study used in this paper to illustrate

the application of these concepts is a simplified

mobile robot application to detect and follow

humans. We rely on the case study presented

in a recently published article (Gascueña &

Fernández-Caballero, 2009a). Several devices

are mounted on the robot to carry out this

functionality. Sonar, bumpers, camera, and

wheels are used for navigation, obstacle avoid-

ing, collision detection, and human detection.

Moreover, we suppose that the camera is able to

at most detect one human at a given instant.

Figure 1 shows as an example the three nested

levels (PL, SL, KL) and the two domains (EOD,

OD) of description of the calculus related to the

camera device. The different concepts depicted

on the figure will be described along the case

study description (see section 5).

In short, the main contribution of our meth-

odological framework is the application of two

different points of view (observer domain and

external observer domain) to describe the types

of agents identified at the three levels (physical,

symbols and knowledge) to understand any

calculation.

Mira and Delgado (2003) state that habitually

the programmer reduces the conceptual model

(KL-EOD) to a formal model (still at the KL,

but now in the OD) and the corresponding

program (in the OD of the SL). Then a compiler

takes care of the last reduction step moving the

program (SL) to the physical level (PL) in order

to be executed into logical circuits. The two next

subsections offer our vision of a computational

agent’s conceptual and formal model, respec-

tively. The two next subsections offer our vision

310 Expert Systems, September 2011, Vol. 28, No. 4 c� 2011 Blackwell Publishing Ltd


of a computational agent’s conceptual and for-

mal models, respectively.

3.1. Computational agent conceptual model

(KL-EOD)

Like in AI and Robotics, in the agency three

basic architectures are distinguished – sym-

bolic, situated and connectionist – to approach

different solutions to a problem, by modeling

data and knowledge and, later, operating the

inferences of the model. An agent is symbolic

when it uses declaratory and explicit knowl-

edge in natural language to describe the con-

stituent organizations (‘concepts’) and the

inference rules. In Robotics this is associated

to ‘deliberative architectures’, which spend

time and a high number of computational

resources in the decision process. An agent is

reactive or situated when knowledge represen-

tation is within two configuration tables of pre-

calculated input and output, usually called

Figure 1: Camera levels.

c� 2011 Blackwell Publishing Ltd Expert Systems, September 2011, Vol. 28, No. 4 311


‘perceptions’ and ‘actions’. Here, the inference

procedure is of ‘reflex’ type, very fast and

adapted for real-time applications. The percep-

tion-action link is also given by a table or an

automaton with few states. It is proper for

monitoring tasks and for the execution phase

of motor planning, where a command is de-

composed into a set of pre-calculated elemen-

tary actions that execute in ‘efficient’ time,

without having to deliberate. An agent is con-

nectionist when knowledge representation is

given in terms of labeled numerical lines, as

much for the inputs as for the outputs, and the

inference functions are adjustable numerical

associators (ANAs). It is difficult to have all

the necessary knowledge for an application.

For that reason, the most frequent situations

demand hybrid solutions, with reactive and

deliberative, and with symbolic and connec-

tionist parts. For that reason, in the agency

paradigm, there are also hybrid architectures

that combine agents of reactive and delibera-

tive type. The reactive part reacts to the events

of the environment without reasoning, whereas

the deliberative part support plans and per-

forms tasks of a higher abstraction level.

Practically all conceptual agent models com-

ply in the scheme of Figure 2. The figure is

inspired in the Luria’s proposals (Luria, 1973)

on the different functional systems (FS) that

cooperate in the interpretation and the control

of the set of activities that an alive being devel-

ops. The scheme consists of four FS basic types

(Mira, 2006): (1) sensorial FS (perception), (2)

motor FS (action), (3) association and decision

FS (decision and planning), and (4) adaptation

and learning FS. Another system (tone and

watch regulator) is superimposed responsible

for regulating the state of tone and supervises

all the rest. The adaptation and learning FS, like

the memory, is distributed on all other FS. This

agent model is also analogous to the proposal by

Newell (1980) with the name of ‘intelligent

agent’. It also agrees with the models usual in

Robotics. The feedback loops introduced here

were proposed by McCulloch and Pitts (1939) to

give support to the homeostasis, autonomy,

purpose and intentionality concepts. These con-

cepts are difficult to formulate and to model

computationally without using the proposed

three nested feedback levels. The type I loops

are closed within each FS (sensorial, motor and

Figure 2: Luria functional systems and McCulloch-Pitts feedback loops, integrated to propose a

general agent model interacting with his means in which, simultaneously, there may be other agents.

312 Expert Systems, September 2011, Vol. 28, No. 4 c� 2011 Blackwell Publishing Ltd


association) and between two or more of these

FS. The type II loops are closed through the rest

of the agent body (proprioception and regula-

tion – homeostasis – of the internal means) and

the loops of type III are closed through the

external environment, using the specific sensors

and effectors of each type of agent to physically

and formally connect it to their environment.

The type I loops have to do with all the

mechanisms necessary to model the perception,

association and action tasks. The type II loops are

associated to the reflex mechanisms, the operating

and instrumental conditioning, the homeostatic

(regulation of the set of variables that characterize

the internal state of the agent at all levels) and

autopoietical (continuous synthesis of the compo-

nents necessary to maintain the identity of the

agent) mechanisms. Finally, the type III loops

have to do with perception-action integration and

‘voluntary’ (autonomous) control of the external

behavior of the agent. The computational version

(KL-OD) of these organizations is far from the

meaning in humans. The great number of type I,

II and III loops that exist in biological systems,

along with memory and structural and functional

plasticity, is an indispensable requirement to

develop adaptive and intelligent capacities in

these systems. Consequently, the degree of auton-

omy and the capacity of adaptation and intelli-

gence of an agent depend on the feedback

mechanisms that are implemented for each con-

crete case. Considering the paradigms of AI as a

classification criterion of different agent types, it

is easy to demonstrate that there are only two

basic types: (1) the ones based on descriptions in

EOD that use declarative knowledge in natural

language and (2) those based on mechanisms of

the OD, causal in the implementation of the three

mentioned levels.

3.2. Formal models for agents

Whichever be the final version of the agent at

knowledge level (KL-EOD), the following phase

is to operationalize its entities and relations, its

data and inferences. A formal model widely used

for the description of abstract agent architectures

is automata theory, leaving open the specification

phase of the functions of state change ( f ) and

output production (g). Let us remember that an

automaton can be specified as follows:

A¼ðX;Y;S; f;gÞ ð1Þ

where X is the finite set of possible inputs

(xi, i¼1 . . . n), Y is the finite set of possible out-
puts (yk, k¼1 . . . p), S is the finite set of possible
internal states (Sj, j¼1 . . . m), f and g are two sets
of decision rules that represent the dynamics

of the system in the production of new states

(rules f ) and in the production of outputs

(rules g). The function of production of new

states, f, is defined in extensive as an application

of the Cartesian product X � S on S, and the
function of production of outputs, g, is an

application of the Cartesian product X � S on Y.

X�S ! f S : SðtþDtÞ¼ f ½xðtÞ;SðtÞ� ð2Þ

X�S !g Y : yðtþDtÞ¼g½xðtÞ;SðtÞ� ð3Þ

That is to say, the new state, S(tþDt), is a
function, f(x, S), of the current state, S(t), and of

the input, x(t). In front of a same input, different

state transitions can take place. As well, the same

input x(t) and the same state S(t) participate

through another function, g(x, S), in the produc-

tion of the outputs. These output variables are the

responses of the automata to the disturbances,

x(t), that are received from the external environ-

ment, which as well is another automaton. That is

to say, a finite automaton always interprets itself

in its relation with external environments so that

the outputs of the automaton are the inputs of the

environment and vice versa. In their formulation

at knowledge level, the inputs, xi, the outputs, yk,

and the states, Sj, are class labels that represent the

static and dynamic roles of the inferences in which

a certain method decomposes the reasoning pro-

cess. Their meaning is established in agreement

with a semantics table, dependent of the ontology

of the application domain. Analogously, functions

f and g are the inferences and the corresponding

control structures. In their formulation at symbol

level the inputs and the states are, again, labels

associated to the entities of the programming

language. Finally, at physical level, the inputs, the

c� 2011 Blackwell Publishing Ltd Expert Systems, September 2011, Vol. 28, No. 4 313


states and the outputs are levels of tension in

analogical or digital electrical signals, and func-

tions f and g are the electronic mechanisms that

connect those signals.

Let us remember that the input and output

spaces are, in general, representation spaces.

Each input, state or output is associated to a

label whose specification in the meaning tables

express in natural language a description of the

situation that is being represented. Following

the commonest agent definition Franklin and

Graesser (1996), literally, ‘an autonomous agent

is a system situated within and a part of an

environment that senses that environment and

acts on it, over time, in pursuit of its own agenda

and so as to effect what it senses in the future’,

the formal description of an agent could be

decomposed into several processes or phases:

1. Identification of the goals (or objectives)

and of the tasks associated to the attain-

ment of these goals.

2. Identification of sets X (‘perceptions’) and Y

(‘actions’) that formally represent the means

of interest for a concrete agent.

3. Identification of set S of internal states

necessary to describe the agent’s dynamics.

The name and type of the labels used in

EOD to specify and to classify these states

depend on the used agent architecture. For

example, in BDI (Georgeff et al., 1998) the

beliefs, desires and intentions are the three

types of basic labels whose description in

extensive gives rise to the elements of the set

S of internal states.

4. Specification of functions f and g used to

describe the necessary inferential rules so

that the agent reaches his goals.

5. Specification of the models, algorithms and

learning mechanisms used to modify func-

tions f and g.

4. Application to surveillance

In the last few decades, the field of surveillance

systems has captured the attention of industry

and research (e.g. Mira et al., 2004; López-

Valles et al., 2007; Fernández-Caballero et al.,

2008a, 2009; Delgado et al., 2010). The coopera-

tion that emerges directly from the sociability

characteristic is an agent’s distinguishing char-

acteristic (Wooldridge & Jennings, 1995). The

capability to communicate makes it possible for

agents to work together to solve complex pro-

blems which cannot be dealt with by a single

agent, this being the essence of MAS (Huhns &

Stephens, 1999).

MAS are noted for the fact that they are made

up of collections of potentially independent and

autonomous agents, usually heterogeneous,

which work together to solve a problem which

goes beyond their individual capabilities. MAS

are appropriate within domains in which the

necessary knowledge to solve a problem is dis-

tributed along different places. The solution to

the problem depends on the coordination of the

tasks to be carried out by different entities with

different capabilities, usually without the super-

vision of a single centralized coordinator. For

example, defense applications are performed in

highly decentralized and heterogeneous envir-

onments and=or require the incorporation of
intelligent decision making. These characteris-

tics make the technologies, techniques and algo-

rithms used within the scope of MAS adequate

to be applied in military domain applications

(Pechoucek et al., 2008) such as logistics,

manned and unmanned air traffic control, simu-

lation and training.

Likewise, on the one hand, Patricio et al.

(2008) highlight the suitability of using a MAS

for video surveillance because (1) the loose

coupling nature of a multi-agent architecture

allows more flexibility in the communication

process, and, (2) the ability to assign responsi-

bilities to each agent is ideal to solve complex

tasks in a surveillance system. These complex

tasks entail the use of coordination and coop-

eration mechanisms and dynamic configuration,

which are widely used in the MAS community

(d’inverno et al., 1997). On the other hand,

intelligence distribution in MAS allows dealing

with questions that turn up in the development

of surveillance systems (bandwidth, productiv-

ity, speed, robustness, autonomy, scalability)

(Pavón et al., 2007). In summary, the basic

314 Expert Systems, September 2011, Vol. 28, No. 4 c� 2011 Blackwell Publishing Ltd


characteristics of the agents suggest that they

are good choices to solve the problems dealt

with in surveillance systems, such as it will be

approached through a study case in this section.

Recently, the use of agent technology in surveil-

lance has been revised (Gascueña & Fernández-

Caballero, 2009b).

All the previous concepts are being applied in

semi-automatic visual surveillance tasks (López

et al., 2006c, 2007; Valencia-Jiménez & Fernández-

Caballero, 2006; Gascueña & Fernández-

Caballero, 2007; Pavón et al., 2007; Martı́nez

et al., 2008) composed of a set of collaborative

cameras installed in a building and a camera-

mounted mobile robot to offer pre-alarms and=or
alarms detected indoor and outdoor. The video

images captured by each of the cameras enable

segmenting and tracking (López et al., 2006a,

2006b; Fernández-Caballero et al., 2008b;

Moreno-Garcia et al., 2010) objects of interest

(obtained as image blobs) with the objective of

providing meaningful events and suspicious activ-

ities (e.g. people roaming or abandoning an

object). The cameras collaborate in the sense of

obtaining richer surveillance observations that are

only available through the fusion of information

captured on various places. Mobile robots could

be equipped with different sensors in order to

perform surveillance tasks because they are able

to obtain a vision of the objects of interest from a

different perspective, and to accede to zones that

are inaccessible to fixed cameras or that are

dangerous for the humans. In particular, the next

section offer a concrete case, in which methodolo-

gical framework concepts described in the previous

sections are put into practice, that introduces the

design of a mobile robot application for the detec-

tion and following of humans with a MAS per-

spective. It is based in Prometheus methodology

(Padgham & Winikoff, 2004). The Prometheus

methodology has been chosen because it provides

a collection of guidelines helping to determine the

elements (for instance, agents and interactions)

that form the MAS. These guidelines are also

helpful to the experts in MAS development. They

will be able to transmit their experience to other

users through explaining why and how they have

obtained the different elements of the agent-based

application. In addition, Prometheus is also useful

as it explicitly considers agent perceptions and

actions as modeling elements. In Robotics, per-

cepts are environment data collected by several

robot sensors (temperature, light, distance, etc)

and actions represent the control carried out by

the robot actuators (motors, LEDs, and so on).

Traditionally, proposals for agent architec-

tures are categorized as reactive, cognitive, or

hybrid just as described in section 3.1. Surveil-

lance systems are sufficiently complex to encom-

pass heterogeneous agent architectures.

Therefore, the internal structure of each identi-

fied agent should be modeled and implemented

using the most suitable technology according to

the functionality to be offered. For instance, in

Robotics it is usual to find reactive, deliberative

and even hybrid components (Qureshi et al.,

2004). For this reason, in our approach the

Prometheus Detailed Design phase will not be

followed, as it imposes the usage of a specific

agent-based model or architecture. Specifically,

the artifacts produced by this phase are con-

ceived with the BDI agent architecture in mind.

5. Mobile robot application for the detection

and following of humans

The process to detect and follow moving objects

used by the robot is depicted in Figure 3. In the

following, a brief description of each state is

introduced:

� Firstly, the robot is moving randomly
around the environment (state Wander)

while movement has been not detected by

the camera.

� On the one hand, when the information pro-
vided by the camera contains a region of

interest (ROI) characteristic of a human then

the robot follows him=her (state Follow). The
robot remains in this state until the camera

loses the target, then it transits to state Wander.

� On the other hand, when the robot is wan-
dering or following a human and the sonar

informs that there is some obstacle at a

minimum distance from the robot then the

robot has to avoid it (state Avoid Obstacle).

c� 2011 Blackwell Publishing Ltd Expert Systems, September 2011, Vol. 28, No. 4 315


In this new state, the robot executes actions

to stop and to orient it towards a new

direction in order to avoid the obstacle

detected. Moreover, the robot considers also

information provided by the bumper in or-

der to move back if a collision is detected

(state Move Back). Then, when the problem

has been solved (SonarAvoidedObstacle) the

robot again wanders or follows a human

according to the information provided by

the camera (CameraNoHumanDetection and

CameraHumanDetection, respectively).

� Notice that for not damaging the robot the
information provided by the sonar has a

greater priority than the camera informa-

tion. For example, if the robot is wandering,

the camera detects a human and the sonar

informs that there is an obstacle at a mini-

mum distance then the robot has to avoid

the obstacle instead of following the human.

5.1. System specification

In the first phase of the Prometheus methodol-

ogy, namely the System Specification phase, the

analysis overview diagram is developed, which

shows the interactions between the system and

the environment (see Figure 4). Several entities

are identified in the diagram:

� Actors. An actor is an external entity –
human or software=hardware – that inter-
acts with the system. At this level, an actor

for each device mounted on the robot (sonar,

camera, bumpers, and wheels) has been

identified.

� Percepts. The information that comes
from the environment has been identified

as percepts. It corresponds to distance to

obstacles=targets perceived by the sonar
(Distance_P), images captured by the cam-

era (Image_P), and impacts detected by the

bumper device (Collision_P).

� Actions. Every operation performed by the
system on the actors is identified as an

action. It corresponds to the commands

to control wheel motion (Set direction_a,

Stop_a, Move_a).

� Scenarios. A scenario is a sequence of struc-
tured steps – labeled as action, percept, goal,

or other scenario – that represents a possible

execution way of the system. There are three

Figure 3: The robot’s states.

316 Expert Systems, September 2011, Vol. 28, No. 4 c� 2011 Blackwell Publishing Ltd


scenarios (Wandering motion scenario, Hu-

man following scenario and Avoiding obstacle

scenario) that correspond to the main states

of the robot.

Notice that the concept of an actor, when referred

to a hardware entity, is directly considered in our

methodological framework as a physical agent.

The idea proposed to model an application com-

posed of several physical devices consists in asso-

ciating, firstly, an actor (physical agent – PL) to

each physical device. In this case, for instance, the

Camera_PL agent is the physical agent associated

to the camera device.

5.2. Architectural design

Once the system requirements and the environ-

ment of the problem have been specified in the

previous phase, the tasks carried out in the next

phase of the Prometheus methodology (that is,

the Architectural Design phase) are to decide

what kind of (new) agents the system will have

and how the interaction between them will be.

These are specified in a system overview diagram.

For example, Figure 5 depicts an excerpt of

the system overview diagram, where only the

agents related to the camera are identified (that

is, Camera_PL, Camera_SL, and Camera_KL

agents) as well as their interactions to control

the robot state. The Camera_PL physical agent,

which was already identified in the System Speci-

fication phase, sends images (Image_P percept) to

the Camera_SL agent. The last agent is respon-

sible of performing an image segmentation pro-

cess to look for a region of interest representing a

human (ROI_D data). This information is used

by the Camera_KL agent to communicate if

a human has been detected to the robot move-

ment manager (RobotControl_KL agent). These

communications are specified inside the Camera

Robot_IP interaction protocol.

5.3. Agent internal structure

The internal structure of each agent identified

previously has now to be developed. Obviously,

usually there is no single agent model able to be

applied for all agents.

Regarding the example of the physical agent

(Camera_PL), you may observe that it is de-

scribed from the two points of view considered

in our framework (see the bottom of Figure 1).

On the one hand, the pixels of the image

captured by the camera represent the informa-

tion of the physical agent from the point of view

of the observer domain (OD). On the other

Figure 4: The Prometheus Analysis Overview Diagram.

c� 2011 Blackwell Publishing Ltd Expert Systems, September 2011, Vol. 28, No. 4 317


hand, the image visualization on a monitor is its

representation related to the external domain

observer (EOD).

In the middle of Figure 1 it may be observed

that the Camera_SL agent is labeled as a sym-

bols agent. From the point of view of the OD

sentences of a segmentation algorithm (if . . .

then . . . else . . .) are incorporated. On the other

hand, the image visualization on a monitor,

including a possible square to highlight the

region of interest that characterize a human

through a series of parameters, is a representa-

tion related to the EOD.

As explained in this paper, the operationali-

zation of the computational agent conceptual

model (EOD-KL) may be formally described as

a finite state automaton (see section 3). There-

fore, it is necessary to select an agent-based

language=framework that offers support to the
implementation. In this sense ICARO-T (Garijo

et al., 2008) satisfies our purposes. So, the model

of an ICARO-T reactive agent represents an

EOD-KL agent in our framework.

Camera_KL agent is located at the knowledge

level in our framework (see the top of Figure 1).

This fragment is shown again for readability

purposes (see Figure 6). Its responsibility is

informing the RobotControl_KL agent when a

human has been detected. So, on the one hand,

from the point of view of the OD the function-

ality of this agent can be described with an

automaton composed of two states. The inter-

pretation is summarized as follows:

� It remains in S0 state whilst no region of
interest (ROI) is detected in two consecutive

frames.

� It remains in S1 state whilst a ROI is
detected in two consecutive frames.

� It transits from S0 to S1 when a ROI is
detected in the current frame.

� It transits from S1 to S0 when no ROI is
detected in the current frame.

On the other hand, from the point of view of

the EOD a meaning is provided at each transi-

tion using concepts of the ICARO-T reactive

agent models (see image located on the left in

Figure 6):

� S0 state is labeled as NoHuman to point out
that no human is detected.

� S1 state is labeled as Human to point out the
opposite situation to NoHuman state, that is,

a human is detected.

� Human and NoHuman labels allow easily
associating concrete situations of the agent

life cycle.

� Three new states have been introduced due
to ICARO-T implementation features,

namely Initial, S-1 and Final states.

� The transition from Initial to S-1 state is the
first executed one. It is satisfied when the

manager agent, which is already implemen-

ted in ICARO-T framework, creates the

Camera_KL agent. In this case, the transi-

tion execution produces the activation of the

camera and sends itself an event startDetec-

tion. The event allows that the Camera_KL

agent starts the detection process and sends

a first event CameraNoHumanDetection to

the robot to communicate that a human is

not detected. After that, Camera_KL tran-

sits to NoHuman state.

Figure 5: The Prometheus System Overview

Diagram.

318 Expert Systems, September 2011, Vol. 28, No. 4 c� 2011 Blackwell Publishing Ltd


� The other transitions are related to the
transitions belonging to the automaton of

the Camera_KL agent described from the

point of view of the OD. The external ob-

server considers that (1) when transition S0

to S0 takes place, then the agent does not

need to communicate to the robot that a

human is not detected, as this was already

communicated previously; (2) when transi-

tion S0 to S1 takes place, the agent needs to

communicate the detection of a human; (3)

when transition S1 to S0 takes place, the

agent communicates that a human is not

detected; and (4) a similar reasoning to

situation (1) is carried out for transition S1

to S1.

� Finally, a particular transition called univer-
sal transition, which is valid for any state of

the automaton, is used. This transition takes

place for a given event. The action is exe-

cuted and the automaton transits to the next

state, regardless of the automaton’s state. In

the Camera_KL agent, if stopDetection event

is received then the deactivation of the cam-

era is produced.

The necessary mechanisms for agent percep-

tion and control are already implemented in

ICARO-T (reactive agent pattern). Therefore,

to implement the behavior for each reactive

agent the developer only needs to specify the

state transition table in XML and the actuation

model (semantic actions). On the other hand, it

is also necessary to highlight that the automaton

depicted on the left in Figure 6 is formally

defined in an equivalent way using the notation

presented in section 3.2. In this case the auto-

maton is deterministic. That is to say, in front of

an input only one state transition can take place.

For the reactive agent to be capable of interpret-

ing the graphically=formally represented auto-
maton it is expressed it in a textual way through

an XML file named ‘automaton.xml’. On the

other hand, actions executed by a reactive agent

are defined as methods of a semantic actions

class.

Keeping in mind section 1, firstly, the system

concepts are defined in natural language terms.

Secondly, it is necessary to formalize them.

Finally, the formal model is translated to some

programming language. Therefore, the model

entities are associated to concepts used in the

selected programming language. Table 3 shows

which model entities are translated into their

equivalent ICARO-T implementation concepts.

We have to highlight that the actor (physical

Figure 6: Camera_KL knowledge agent.

c� 2011 Blackwell Publishing Ltd Expert Systems, September 2011, Vol. 28, No. 4 319


agent) concept is associated with the resource

concept, which can be used by the agents to

interact with the environment (execute actions)

and it sends events towards the agents to trans-

mit the information gotten from the environ-

ment (receive percepts). The symbols agent

concept is also implemented as a resource. In

this way it easily offers to other agents or

resources the information obtained. They access

the information using the interface of use of

the resource. The knowledge agent concept is

implemented as a reactive agent automaton as

illustrated in the case study. The percept and

message concepts used in the modeling to repre-

sent a communication between agents are both

translated as an event.

6. Conclusions

In this paper the agent concept has been faced

from a computational perspective. The concept

of computational agent is located within the

methodological framework of levels and do-

mains of description of a calculus in the context

of different usual paradigms in AI (symbolic,

situated, connectionist, and hybrid). Therefore,

it has been shown that a computational agent

must specify its conceptual model, its formal

model and its implementation, starting from the

set of functional specifications available on its

goals, activities and tasks. Moreover, the points

of view from the observer domain and external

observer domain are illustrated to describe the

structure of the specific agents identified, in the

mobile robot case study, at the three different

levels (physical, symbols and knowledge) neces-

sary to understand any calculation.

We are currently engaged in modeling the visual

surveillance task. Surveillance systems consist of a

great diversity of entities that have to cooperate in

highly dynamic and distributed environments.

The use of agents for their control allows a greater

degree of autonomy and response because of

their capabilities to adapt and to cooperate. This

is why, after introducing some methodologies

and programming languages for computational

agents, the ideas presented are applied on semi-

automatic surveillance systems. We have used

Prometheus as agent-oriented SE methodology

and ICARO-T framework as development frame-

work. Two case studies are described to exemplify

the concepts introduced. Thus, a mobile robot

application for the detection and following of

humans and a collaboration application between

surveillance cameras are provided.

Acknowledgements

This work was partially supported by Spanish

Ministerio de Ciencia e Innovación TIN2010-

20845-C03 grant, and by Junta de Comunidades

de Castilla-La Mancha PII2I09-0069-0994 and

PEII09-0054-9581 grants.

This article is dedicated to the memory of

Professor José Mira, a great researcher, a wise

man, a loving husband, and a close friend; but

who sadly passed away on August 13, 2008.

This paper is an extended version of a recent

conference paper (Mira et al., 2009).

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Table 3: Mapping from modeling concepts to ICARO-T implementation concepts

Modeling concept ICARO-T concept

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Symbols agent Resource
Physical agent (Actor) Resource
Percept Event
Message Event
Action Used in a semantic action and defined as a method in a resource
Percept and message Used as input in the automaton definition

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The authors

José M. Gascueña

José M. Gascueña received his MSc in Compu-

ter Science from the University of Castilla-La

Mancha at the Superior Polytechnic School of

Albacete, Spain, in 2004. In 2006, he received a

scholarship from the Spanish Junta de Comuni-

dades de Castilla-La Mancha. In 2010 he got his

Ph.D. from University of Castilla-La Mancha in

applying multi-agent systems technology in the

computer vision area. His research interests are

in Software Agents and Multi-agent Systems, as

well as in Computer Vision.

Antonio Fernández-Caballero

Antonio Fernández-Caballero received his de-

gree in Computer Science from the Technical

University of Madrid, Spain, in 1993, and re-

ceived his Ph.D. from the Department of Artifi-

cial Intelligence of the National University for

Distance Education, Spain, in 2001. He is a Full

Professor with the Department of Computer

Science at the University of Castilla-La Man-

cha, Spain. He is the director of the research

group n&aIS (natural and artificial Interaction

Systems) belonging to LoUISE (Laboratory of

User Interaction and Software Engineering) of

the Albacete Research Institute of Informatics,

Spain. His research interests are in Image Pro-

cessing, Cognitive Vision, Neural Networks,

and Intelligent Agents. A. Fernández-Caballero

is an Associate Editor of the Pattern Recogni-

tion Letters journal, a member of the Editorial

Board of the Journal of Physical Agents, and a

member of the IAPR. He has authored around

200 peer reviewed papers.

Marı́a T. López

Marı́a T. López received her degree in Physics

from the University of Valencia, Spain, in 1991,

and received her PhD from the Department of

Artificial Intelligence of the National University

for Distance Education, Spain, in 2004. Since

1991, she is an Associate Professor with the

Department of Computing Systems at the Uni-

versity of Castilla-La Mancha, Spain. Her re-

search interests are in Image Processing and

Computer Vision. Marı́a T. López is member

of the IAPR.

Ana E. Delgado

Ana E. Delgado is a Full Professor of Computer

Science and Artificial Intelligence with the De-

partment of Artificial Intelligence at the Na-

tional University for Distance Education

(UNED) in Madrid, Spain. Her current research

interests are in Neural Modeling, Bio-inspired

Cooperative Agents and Computer Vision.

c� 2011 Blackwell Publishing Ltd Expert Systems, September 2011, Vol. 28, No. 4 323