HIVE-BDI: EXTENDING JASON WITH SHARED BELIEFS

AND STIGMERGY

Matteo Barbieri and Viviana Mascardi

Department of Computer Science (DISI), University of Genova, Genova, Italy

Keywords:

BDI agents, MASs, Stigmergy, Belief sharing, Agent coordination.

Abstract:

The classic BDI model focuses on the internal functioning of a single-agent architecture. Neither shared

beliefs nor spontaneous and indirect coordination via the environment are supported. We describe Hive-BDI,

an extension of the Jason BDI-style language with shared beliefs implemented via the logic-based coordination

language ReSpecT and with stigmergy obtained via digital pheromones. A case study where robots roaming

an unknown environment collaborate for creating a map demonstrates the feasibility of our approach.

1 INTRODUCTION

The classic BDI model (Rao and Georgeff, 1991) fo-

cuses on the internal functioning of a single-agent ar-

chitecture: the way events taking place in the envi-

ronment are perceived and the way agents can orga-

nize themselves by sharing mental attitudes are not

addressed. No form of spontaneous and indirect coor-

dination via the environment (stigmergy (Bonabeau,

1999)) is supported, and information belonging to an

agent’s private set of beliefs can be shared only by ex-

plicit communication. Many extensions of this basic

model to cope with groups, teams and organizations

formation have been proposed in the literature (Hep-

ple et al., 2008), and the research interest in achieving

agent coordination via stigmergy has increased in the

last ten years (Valckenaers et al., 2001; Parunak et al.,

2002; Ricci et al., 2007).

The usefulness of a BDI framework supporting

both belief sharing and stigmergy is easy to under-

stand. Imagine a simple scenario where several robots

governed by rational agents are roaming an unknown

environment, collecting data as they move and pro-

cessing them in order to create a shared map. In the

classic BDI model, a previously designated coordina-

tor agent should act as a “shared blackboard” so that

all information collected by every agent in the system

can coexist in the belief base of a single agent. If an

agent needed information about an area inspected by

another agent, he should query the coordinator. Also,

two or more agents that wanted to coordinate in order

to deﬁne a joint strategy should adopt sophisticated

negotiation protocols mediated by the coordinator.

Instead of an adaptation of the existing BDI

model, a new model where each agent, still retain-

ing his personal belief base, can also access a shared

knowledge base might prove suitable for avoiding

bottlenecks and for ensuring fault-tolerance. Shared

information should be immediately available to every

other agent in the MAS or in the group of registered

agents and coordination should be achieved via the

environment.

Despite of the advantages of beliefs sharing and

stigmergy, to the best of our knowledge none among

the most widespread BDI frameworks has been ex-

tended yet to support both features. To overcome

this limitation we designed and implemented Hive-

BDI that extends Jason (Bordini et al., 2007) with

shared beliefs by taking advantage of the logic-based

coordination language ReSpecT (Omicini and Denti,

2001), and with stigmergy by introducing digital

pheromones.

The paper is organized in the following way: Sec-

tion 2 describes the technologies upon which Hive-

BDI grounds and analyses the related work; Section

3 gives hints on the Hive-BDI implementation and

brieﬂy discusses Hive-BDI at work; Section 4 con-

cludes.

479

Barbieri M. and Mascardi V..

HIVE-BDI: EXTENDING JASON WITH SHARED BELIEFS AND STIGMERGY.

DOI: 10.5220/0003291504790482

In Proceedings of the 3rd International Conference on Agents and Artiﬁcial Intelligence (ICAART-2011), pages 479-482

ISBN: 978-989-8425-41-6

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

2 BACKGROUND AND RELATED

WORK

Belief Sharing in BDI Settings. Some approaches

and methodologies for belief sharing in MASs already

exist: Glinton et al. (Glinton et al., 2010) present a

formal model and methodology for analyzing and de-

signing two types of multi-agent belief sharing mod-

els, based on Potts and Random Cluster Statistical

Mechanics models respectively, and distinguished by

the mechanism used for information exchange. Other

works by the same research group address the be-

lief sharing problem by taking a perspective of strat-

egy optimization (Velagapudi et al., 2007; Velagapudi

et al., 2008). However, very few attempts to address

belief sharing in BDI languages and frameworks have

been made. According to a recent paper by Hepple

et al. (Hepple et al., 2008) that analyses the state-of-

the-art in agent organization in BDI languages, only

Pynadath et al.’s TEAMCORE (Pynadath et al., 2000)

takes belief sharing as a characterizing feature for

agent organizations ((Hepple et al., 2008), p. 82, Ta-

ble 1). An approach similar to Pynadath’s one is dis-

cussed in (Vieira et al., 2007) where, as examples of

advanced communication features, Vieira et al. show

plans that allow agents to reach shared beliefs and en-

sure that agents are kept informed of the adoption of

their goals by other agents.

Stigmergy in Cognitive MASs. Stigmergy

(Bonabeau, 1999) is a mechanism of spontaneous,

indirect coordination between agents, where the trace

left in the environment by an action stimulates the

performance of a subsequent action, by the same

or a different agent. Stigmergy can be integrated

in a MAS as a different method of communication

between agents, which exchange information by al-

tering the environment releasing digital pheromones

(Dorigo et al., 1996).

We are aware of only three works dealing with

stigmergy in MASs consisting of cognitive agents. In

(Ricci et al., 2007), the impact of stigmergy on the

structure and organization of MASs based on cog-

nitive agents is explored. Coffey and Clark present

the structure of a hybrid architecture for robot control

where a BDI-style planning layer manipulates a plan

library in which plans are comprised of hierarchical,

suspendable and recoverable teleo-reactive programs

(Coffey and Clark, 2006). A shared belief store can

be used by groups of agents. A foraging agent sce-

nario where an ant-style pheromone-laying algorithm

directs the agent’s search by guiding it away from pre-

viously explored areas in a pseudo-random walk is

used to demonstrate the approach. Because of the in-

tegration of shared beliefs and stigmergy, Coffey and

Clark’s work is very close to ours, although it uses a

very different infrastructure. Finally, in (Piunti and

Ricci, 2009) Piunti and Ricci investigate the use of

cognitive artifacts in MAS, as computational entities

designed to store, process and make available infor-

mation relevant for agents to coordinate their coop-

erative distributed activities. The feasibility of their

proposal has been experimented in Ja-Ca (Jason +

CArtAgO, a platform for developing artifact-based

environments for MAS (Ricci et al., 2008)). Although

their work does not explicitly deal with stigmergy, it

is worth mentioning since it might easily support it

and because it is based on Jason, as ours.

Jason. Jason (Bordini et al., 2007) is a Java-based

interpreter for an extended version of AgentSpeak

(Rao, 1996) which allows a high level of customiza-

tion. Our main extension to Jason affected the Be-

liefBase class - as far as belief sharing is concerned

- and the Environment class - as far as the digital

pheromones are concerned. The purpose of Belief-

Base class is to manage the agent’s beliefs. Three

methods handle tuple addition, removal and lookup

(checking whether a tuple exists in the belief base).

The Environment class implements the simulated en-

vironment in which agents are situated. Its purpose

is to update agents’ percepts and react to their ac-

tions upon itself. The default Jason library includes

two classes which may be used to implement an envi-

ronment represented as a grid: GridWorldModel and

GridWorldView.

ReSpecT. ReSpecT (Omicini and Denti, 2001) is a

logic-based coordination language aimed at deﬁning

the behavior of tuple centers in order to coordinate

autonomous computational entities, such as software

agents. Written in Java, its package includes sev-

eral components, in particular it provides Java classes

for the creation and management of Tuple Centers,

namely blackboards where agents can write data in

the form of tuples. There are ﬁve main primitive op-

erations which can be executed upon a ReSpecT Tu-

ple Center (TC): out(T) writes a new tuple T on the

TC; rd(T) retrieves a tuple X unifying with T from

the TC (blocking); in(T) extracts a tuple X unifying

with T thus deleting it from the TC (blocking); rdp(T)

is the non-blocking variant of rd(T); and inp(T) is the

non-blocking variant of in(T).

3 HIVE-BDI AT WORK

In this section, we provide a short overview of the

Hive-BDI implementation and an example of use.

ICAART 2011 - 3rd International Conference on Agents and Artificial Intelligence

480

More details on both issues can be found in (Barbi-

eri, 2010).

Shared Beliefs. The Hive-BDI shared knowledge

base is implemented with a ReSpecT Tuple Center

to which each agent’s BeliefBase Class connects via

a Socket. The shared knowledge base is composed

of two parts: the Hive Belief Base (referred to as

HiveBB in the sequel) and the Hive Server. Since

the Hive-BDI model still retains personal agent’s be-

liefs which are not accessible from the outside, each

time an operation on agent’s beliefs is invoked, it is

necessary to distinguish whether the belief must be

added, removed or searched for in the shared belief

base or in the agent’s private one. The distinction is

possible thanks to the mustshare annotation that the

Hive-BDI programmer must add to those agent’s be-

liefs that will be stored in the shared belief base. The

HiveBB is implemented as an abstract class that ex-

tends the DefaultBeliefBase Jason class. The Hive

Server is a daemon which can run on a remote ma-

chine and whose purpose is storing shared beliefs and

making them available to the agents in the system.

After deciding that a belief must be shared, a con-

nection with a remote Hive Server is established by

the HiveBB class. Then all information required for

the transaction is encapsulated in an object sent via a

socket to the Hive Server where it is unpacked and the

request is processed. Then another object is created

and ﬁlled with response data (the result of requested

operation and additional data.

An agent acts upon his belief base in three

ways: adding, retrieving, deleting a belief to/from his

knowledge base. Adding a new belief to the shared

belief base is a little more complex than just writing

a new tuple to the tuple center: this because in Ja-

son’s default implementation, if we want to add a be-

lief to the Knowledge Base and another belief having

the same functor, arity and arguments but different an-

notations

is already present in it, the new belief is not

added: instead its annotations are merged with those

of the belief already present. Retrieving a belief is

actually quite a simple operation to implement, since

the method to override does not really take care of

selecting which belief to return: its task is just to nar-

row down the range of beliefs suitable for uniﬁcation

with the one passed as parameter. The default imple-

mentation coming with Jason selects beliefs having

the same functor and arity as that one. Finally, dele-

tion of a belief actually deletes the source of the belief

itself from among its annotations. The belief is actu-

ally deleted when it has no more sources. The method

Two beliefs having these characteristics will be called

similar.

responsible for belief deletion takes this behavior into

account.

Digital Pheromones. In order to implement a

pheromone-based stigmergy mechanism we cre-

ated a new HouseWorldEnvironment class that in-

cludes properties model and view (whose classes

are respectively HouseWorldModel and HouseWorld-

View, which, in turn, extend GridWorldModel and

GridWorldView). We also deﬁned a new entity

pheromones. Since we need to store information

about the “scent” and intensity of pheromones (i.e.

which agent released it and the intensity of the

trail), the HouseWorldModel class has a private prop-

erty pheromones holding a bi-dimensional array of

Pheromone objects. To make an example, if a cell

of coordinates (3, 7) contains pheromones, all other

information (intensity and scent, in this case) can be

extracted from the object in pheromones[3][7]. Each

time an agent moves in the environment he releases

pheromones in the cell where he ends his move-

ment. The release of pheromones is implemented ba-

sically in two steps inside the addPheromones method

in the HouseWorldModel: ﬁrst a check is made to

see whether the (x, y) cell where the agent currently

stands is already marked as containing pheromones.

Then a new Pheromone object is created and put

in pheromones[x][y], potentially replacing informa-

tion about pheromones already present in that cell.

The constructor of class Pheromone takes two argu-

ments: a string which identiﬁes the agent releasing

pheromones, and the intensity of the trace left which

determines how long the trace will remain on the cell

before disappearing because of evaporation.

Hive-BDI at Work: Mapping an Unknown En-

vironment. We validated the potential of a model

such as Hive-BDI to implement a MAS where a few

explorer agents coordinated by a leader named queen,

coherently with the Hive metaphor, are able to build

a map of an unknown environment. Explorer agents

represent robots initially placed more or less ran-

domly in the environment whilst the queen has no

physical incarnation. Once the simulation starts, each

explorer begins to map the room he is in, then moves

to the next unexplored room and so on until the whole

environment has been mapped. Each time an agent

perceives pheromones belonging to someone else he

starts following the trail until he meets the agent who

released it: at this point they merge the information

collected up to that moment, adopting a common ref-

erence system for coordinates and thus updating their

beliefs. Figure 1 shows the graphical output of a sim-

ulation run. Blue circles are agents, green tiles rep-

resent pheromone marks released by agents. Differ-

HIVE-BDI: EXTENDING JASON WITH SHARED BELIEFS AND STIGMERGY

481

ent shades of green represent pheromones having dif-

ferent intensity. The experiments we carried out on

this simple case study were encouraging and demon-

strated the suitability of the Hive-BDI model and of

its implementation to cope with distributed coordina-

tion problems of this kind.

Figure 1: Screenshot taken from a simulation run.

4 CONCLUSIONS

Hive-BDI is suitable for modeling MASs composed

by a relatively large number of agents with limited

capabilities: the system draws its strength from the

cooperation and coordination of agents, giving birth

to an emerging intelligence.

Of course more complex agents, fully exploiting

the BDI potential, can be part of the MAS: the re-

sult would be a MAS involving agents that, despite

being homogeneous and fully interoperable, can be

clustered in different layers according to their reactiv-

ity/deliberativity. More reactive layers would consist

of many simple agents fully exploiting the Hive-BDI

features whereas more deliberative layers would con-

sist of “pure” Jason agents.

Using simpler entities means having less require-

ments in terms of computational and material re-

sources. This may be a desirable feature in real sce-

narios where software agents control physical robots.

When talking about robots, limited capabilities (e.g.,

limited strength or storage capacity, in the case of a

cleaning robot) may imply limited size, thus allowing

such machines to operate in environments otherwise

precluded to bigger ones.

As far as the future of this research activity is

concerned, we are working at enhancing the shared

knowledge base class by fully exploiting the ReSpecT

tuple center and we are experimenting Hive-BDI on

other case studies.

To conclude, the possibility given by Hive-BDI to

deﬁne conditions in an agent’s plan’s context, refer-

ring to the shared knowledge of the hive cluster rather

than the agent’s personal belief base really allows to

think of the MAS as a single entity, and therefore to

write plans accordingly: this actually would repre-

sent a paradigm shift in Multi Agent System program-

ming.

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