=Paper= {{Paper |id=Vol-1848/CAiSE2017_Forum_Paper3 |storemode=property |title=Enriching Business Artifacts with Coordination |pdfUrl=https://ceur-ws.org/Vol-1848/CAiSE2017_Forum_Paper3.pdf |volume=Vol-1848 |authors=Matteo Baldoni,Cristina Baroglio,Federico Capuzzimati,Roberto Micalizio |dblpUrl=https://dblp.org/rec/conf/caise/BaldoniBCM17 }} ==Enriching Business Artifacts with Coordination== https://ceur-ws.org/Vol-1848/CAiSE2017_Forum_Paper3.pdf

Enriching Business Artifacts with Coordination

Matteo Baldoni, Cristina Baroglio, Federico Capuzzimati, Roberto Micalizio

Università degli Studi di Torino — Dipartimento di Informatica
c.so Svizzera 185, I-10149 Torino (Italy)
{firstname.lastname}@unito.it

Abstract. This paper proposes to enrich the artifact-centric approach in two
ways. First, relying on the Agent-Oriented Paradigm (AOP), the tasks acting on
artifacts are organized in agents, seen as autonomous loci of control, whose exe-
cution is goal-driven. Second, the business artifact model is complemented by a
normative dimension. Norms are used to represent the data lifecycle in a form that
is inspectable and reasoned upon by agents. Agents can therefore create expec-
tations about the behaviors of others and hence, leveraging on the norms, agents
can act on an artifact so as to entice, or oblige, others to act themselves. The paper
discusses the advantages and consequences of this norm-aware enrichment, and
outlines a possible realization based on social commitments.

Keywords: Business Artifacts, Normative MAS, Social Commitments

1 A Normative Approach to Business Artifacts
The artifact-centric approach [5,9,8] is recently emerging as a viable solution for spec-
ifying and deploying business operations by combining both data and process as first-
class citizens. In particular, the notion of Business Artifact, initially proposed by Nigam
and Caswell [11], opened the way for the development of a data-driven approach to the
modeling of business operations. The data-driven approach counterposes a data-centric
vision to the activity-centric vision, traditionally used when workflows are explicitly
modeled in terms of processes. Artifacts are concrete, identifiable, self-describing chunks
of information, the basic building blocks by which business models and operations are
described. They are business-relevant objects that are created and evolve as they pass
through business operations. They include an information model of the data, and a life-
cycle model, that contains the key states through which the data evolve, together with
their transitions (triggered by the execution of corresponding tasks). A change to an
artifact can trigger changes to other artifacts, possibly of a different type. The lifecycle
model is not only used at runtime to track the evolution of artifacts, but also at design
time to understand who is responsible of which transitions. In [6], the artifact-centric
model is at the basis of the BALSA data-centric declarative model of business oper-
ations. The BALSA methodology can be summarized in three steps: 1) identify the
relevant business artifacts of the problem at hand and their lifecycles, 2) develop a de-
tailed specification of the services (or tasks) that will cause the evolution of the artifact
lifecycles, 3) define a number of ECA-rules (Event-Condition-Action) that create asso-
ciations between services and artifacts. ECA-rules are the building blocks to define, in

X. Franch, J. Ralyté, R. Matulevičius, C. Salinesi, and R. Wieringa (Eds.):
CAiSE 2017 Forum and Doctoral Consortium Papers, pp. 17-24, 2017.
Copyright 2017 for this paper by its authors. Copying permitted for private and academic purposes.
a declarative way, processes operating on data. A way to coordinate different processes
operating on the same artifacts is by means of choreographies. However, BALSA does
not specify how choreographies should be used to coordinate declarative processes.
Although the BALSA model is extremely interesting and introduces a novel per-
spective on the modeling of business processes, we deem the absence of an explicit
representation of the coordination structure within the model a significant flaw. In par-
ticular, in inherently destructured settings, as cross-organizational interactions, the in-
volved actors are all peers, each of which has its own business goals, and acts in an
autonomous way. Each actor does not know and does not care about the possible goals
of the others. Nevertheless, actors generally need to interact to achieve goals they would
not be able to achieve alone. The interaction is a critical dimension that need to be ex-
plicitly modeled to coordinate the usage of shared resources. This poses the question of
how to scale the business artifact model to coordinate autonomous entities.
We see in the introduction of a coordination model within business artifacts the way
to achieve this goal, and explain what we mean with a simple example. Let us consider
a purchase scenario, involving a merchant and a client. We claim that in order to coordi-
nate the interaction between the two agents, it is necessary to add to the plain message
exchange (which standard approaches to business processes envisage as the only means
of interaction), one further abstraction that explicitly represents the engagements each
player has towards the other. We also claim that business artifacts should trace such
engagements and their evolution, in order to enable an effective agent coordination. For
example, when offering to sell some goods, the merchant commits to the client to ship
the items the client will pay for. Such a commitment is stored by the business artifact
involved in the interaction between the two. Because of his awareness of such a com-
mitment, the client, having paid for the goods, expects the shipment to occur. If this does
not happen, the commitment progresses into a state of “violation” and this information,
stored in the business artifact, provides a proof of the merchant’s misbehavior. From
a different perspective, a client is enticed to use a business artifact by the merchant’s
commitment, which makes explicit the course of interaction the merchant binds to, and
creates a right on the client that such an expected course of action be respected (i.e., my
payment will put an obligation on the merchant to ship the bought items or the merchant
will violate the commitment). On the other hand, the merchant uses commitments inside
the business artifact to entice interactions with potential clients – indeed, the obligation
yielded by a commitment is activated only if a client pays for some goods.
In the example, the commitments that go along with a business artifact make explicit
the behavior the agents are expected to stick to. They also have a normative flavour, as
diverging behaviors will be considered as violations. This awareness causes agents to
take part to an interaction only if they are fine with the commitments. As such, com-
mitments provide a standard to define standards of interaction mediated by business
artifacts. To realize this vision, we claim that (1) services should be encapsulated and or-
ganized into goal-oriented containers; (2) it is necessary to introduce a normative layer.
For what concerns (1), the Agent-Oriented Paradigm (AOP), briefly introduced in Sec-
tion 2, is a good candidate. In particular, the Agent and Artifact meta-model (A&A) [12]
has already shown how artifacts can be used as environment components that mediate
agents’ interactions. However, artifacts in the A&A model are radically different from

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the business artifacts because they do not come with an explicit information model for
data, and they do not exhibit data lifecycles. Thus, this information cannot be exploited
at design time, nor at runtime, to reason about which actions should be taken towards
the achievement of an agent’s goals. Concerning (2), the normative layer would provide
an explicit representation of the business artifacts lifecycles, and of how coordination
is expected to occur. Such a representation would allow agents to reason about the use
of business artifacts and to create mutual engagements for driving their activities. In-
deed, we envisage engagements as encoding causal relations between the actions of an
agent and the goals and actions of another, with a normative power that would allow
each agent to have expectations on the behavior of the others. In the purchase example,
it is easy to see how the introduction of a norm in form of the commitment whenever
a customer pays, the merchant will ship the goods, would enhance coordination.The
customer now knows that after service pay, the merchant will be pushed to consider the
service ship-goods as one of its next goals, otherwise it will violate the norm and will be
sanctioned for that. This provides the customer a guarantee about the achievement of its
own goal (or to recoup its losses). An explicit normative layer plays a central role both
at the design time, to verify whether all the engagements can converge towards their
satisfaction, and at running time to monitor the execution of a system and determine
the violation of engagements. In this paper we introduce the notion of normative busi-
ness artifacts as a means to extend the artifact-centric approach with a normative layer,
where engagements and norms are expressed in terms of social commitments [13]. The
introduction of a normative layer in the more general setting of business processes is
seen as desirable also in [15].

2 Coordination via Normative Business Artifacts

Business artifacts are, by definition, data-aware. They consider data as a first-class prim-
itive that drives the process modeling [5]. Artifacts, however, are not an end in them-
selves. They are business relevant entities that are created, accessed, and manipulated
by different services along a business process. Business artifacts, however, are also re-
sources on whose use interacting parties coordinate. We now show how to introduce a
normative layer that supports such coordination.

Introducing Goal-oriented Containers. Destructured business processes call for a mod-
ularization of the control flow. AOP [7,17] is conceived exactly for handling multiple
and concurrent control flows. Two elements are central in AOP: the agents and the
environment. Agents, as abstractions of processes, possess their own control flow, sum-
marized as the cyclic process in which an agent observes the environment (updating its
beliefs), deliberates which intentions to achieve, plans how to achieve them, and finally
executes the plan [7]. Beliefs concern the environment. Intentions lead to action [17],
meaning that if an agent has an intention, then the expectation is that it will make a
reasonable attempt to achieve it. In this sense, intentions play a central role in the se-
lection and the execution of actions, which represent the innate capabilities agents have
to modify their environment. Among others, (business) artifacts (see A&A-meta model
[12]) are privileged elements of an environment. In particular, in contexts where agents

19
cannot achieve their goals on their own, but need to interact with other agents to do so,
artifacts provide shared resources that agents will use to mediate their interactions.
Introducing Norm-awareness. We claim that business artifacts should be norm-aware
in two ways. First, the lifecycle of a business artifact should be made explicit by way
of norms that specify the rules by which the data evolve. The agents (i.e., the artifact
users), will be able to inspect and reason upon them to decide if and how to operate
on an artifact to obtain some result. Second, agents need to coordinate and regulate
their interaction in using the artifacts to achieve their goals. Given these two bodies of
norms, agents will apply reasoning techniques to plan proper coordination that, possibly
without violating any norm, will lead to goal achievement. This is possible because
norms enable the creation of expectations and commitments among agents. Moreover,
given an explicit representation of such elements it will be possible to realize systems
of accountability to discourage or to detect and explain deviant behavior [4].
Even though data-awareness and norm-awareness are by and large orthogonal to
BDI [17] notions, it is natural to think of agents as BDI agents for a seamless integration
of all the aspects of deliberation, including the awareness of data and of their lifecycles.
For instance, an agent, that is involved in handling orders, may conclude that, since it
has to pick up three items in the warehouse, since each such item is to be packed, since
all packagings are performed by a same other agent, and since one of its goals is saving
energy, it is preferable to pick them up altogether, and deliver them to the other agent
only afterwards, instead of picking and delivering one item at a time. Data-awareness
here is awareness that three items of a same kind are requested. Norm-awareness that
items are picked because each of them is part of some order, whose lifecycle says that
after being picked they will be packed. Again data-awareness allows our agent to know
that all parcels are to be made by a same other agent.
Relying on AOP is promising also because a the agent-based model allows to nat-
urally tackle the issue of coordination by introducing the concept of norm [16]. The
deliberative cycle of agents is affected by the norms and by the obligations these norms
generate as a consequence of the agents’ actions. The limit of current AOP approaches
is that they provide no holistic proposal where constitutive norms are used also to spec-
ify data operations, and where regulative norms are used to create expectations on the
overall evolution of the system (agents behavior and environment evolution).
Environment/Information systems based on normative business artifacts. Figure 1 de-
scribes the high-level architecture of the kind of system we imagine: (1) involving busi-
ness artifacts and agents (with their goals), and (2) holistically norm-aware. Agents in-
teract with each other and with the environment by creating and modifying data which
belong to an information system and that are reified by business artifacts. They are
goal-driven and capable of coordinating with other agents by creating and exploiting
commitments, obligations, permissions, and prohibitions. The conceptual model of the
information system is described in terms of the norms that regulate the evolution of
data, that is, data lifecycles, capturing how data pass from one state to another as a con-
sequence of actions that are performed by some agent. Moreover, business artifacts will
include all those normative elements that regulate the coordination of the agents that
interact by way of the artifact. All this information is available to the interacting agents
in a form that allows agents to reason on it. The agents are aware of the current state

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Environment/Information System
Agent
Norm-based reason on
reason on Business
Process
Data Lifecycle

evolve
dispatch tasks
Agent
use/update

Data
dispatch tasks
use/update

Fig. 1. Environment/Information system based on normative business artifacts.

(of the lifecycle) of the data, as well as of the obligations, prohibitions, commitments,
permissions put on them, and thus they are aware of the tasks expected of them and of
their parties. At any time it is possible to check the execution, identifying pending tasks
and who is responsible of them, while behaviors that violate some norm (e.g. some obli-
gation or some commitment) will be automatically detected, e.g. causing the activation
of procedures that are specifically designed to handle the case. As a final remark, at
design time, norms would provide a programming interface between agents and their
environment, given in terms of those state changes in the environment that are relevant
to the agent and that the agent should tackle.

3 An Exemplification with 2COMM
We now show how the above concepts can be implemented by relying on the 2COM-
M/JaCaMo+ framework [3]. We refer to an implementation where the BDI agents are
implemented in the Jason agent programming language, and where agents share arti-
facts, whose creation and manipulation involves an explicit creation and manipulation
of social commitments. Social commitments [13] provide the normative layer and en-
able the coordination of the goal-driven agents. We show how the norm-driven artifact-
based coordination of agents is realized in the purchase scenario. A social commitment
C(x, y, s, u) captures that agent x (debtor) commits to agent y (creditor) to bring about
the consequent condition u when the antecedent condition s holds (s and u are conjunc-
tions or disjunctions of events). Only the debtor of a commitment can create it. When s
is true the commitment is detached and turns into an obligation on the debtor. When u
is true the commitment is satisfied. A detached commitment that is canceled or whose
consequent becomes f alse is violated. The business artifact, besides representing the
chunk of information at issue, maintains the created commitments, that can be inspected
by the agents. Specifically agents will be notified of the changes to the business artifact
state which include changes occurred to the commitments. Among other events, they
will be aware of the detachment of commitments of which they are debtors, and of the
satisfaction (violation) of commitments of which they are creditor.
The example implementation involves a merchant, selling on-line, and a customer
agent. The merchant advertises some item and specifies the number of available units.

21
Its goal is to be paid for the sold units. The goal of the customer is to get the goods it
is interested in. A customer starts an interaction by requesting a quotation for a num-
ber of units. When the merchant sends the quotation he also creates a commitment
C(merchant, customerId, accepted(price, quantity, customer), goods(customer)), mean-
ing that he commits with the customer that if the quotation is accepted, he will have the
goods delivered to the customer. The customer is, thus, enticed to accept the quotation
because the presence of the commitment, as part of the information provided by the
business artifact, yields that this action will create an obligation on the merchant to de-
liver the goods that will make him achieve his goal. Should delivery not occur, thanks
to the explicit representation of the commitment within the business artifact, and be-
cause of the violation of the obligation expressed by it, it would be possible to identify
the merchant as the liable1 party. The payloads (quotation, price, quantity) are stored in
the business artifact. On the other hand, acceptance of a quotation binds the customer
with the merchant to pay, by the creation of the commitment C(customer, merchant,
goods(customer), paid), leading the merchant to satisfy his goal. After payment, the
merchant is expected to send a receipt. This expected interaction involves the execution
of operations that are exposed by the business artifact (quote, ship and emitReceipt for
the merchant; request, accept, reject and sendEPO for the customer).
1 +requestedQuote ( Quantity , Customer Id )
2 <− q u o t e ( 1 0 0 0 , Q u a n t i t y , C u s t o m e r I d ) .
3 + c c ( My Role Id , C u s t o m e r R o l e I d , a c c e p t ( P r i c e , Q u a n t i t y , C u s t o m e r R o l e I d ) ,
4 Goods , "DETACHED" ) : e n a c t m e n t i d ( My Role Id )
5 <− s h i p ( C u s t o m e r R o l e I d , Q u a n t i t y ) .
6 + c c ( My Role Id , C u s t o m e r R o l e I d , p a i d ( C u s t o m e r R o l e I d ) , R e c e i p t , "DETACHED" )
7 : e n a c t m e n t i d ( My Role Id )
8 <− e m i t R e c e i p t ( C u s t o m e r R o l e I d ) .

Above, an excerpt of the merchant agent program. The merchant is solicited to act by
the reception of a requestedQuote event, that comes from a customer through the busi-
ness artifact. The execution of quote sends a quotation to the customer and causes the
creation of the merchant’s commitment to send the goods if the quotation is accepted.
The detachment of such a commitment (due to an event raised by the customer) is
notified to the merchant by the artifact alongside with the relevant information. The
merchant will now try to satisfy the commitment by executing ship. The other commit-
ment is detached when the customer pays, causing the merchant to emit a receipt. It
is, thus, possible to observe how coordination is regulated by the commitments. Since
commitments are created by their debtors, it is natural to assume that such debtors will
have the code for tackling their detachment.

4 Conclusions

The presented work is strictly related to the problem of interaction in multiagent sys-
tems. In these systems, interaction is mainly focused on the modeling of communi-
cation patterns (protocols), which are concerned with the sequence of messages that
can be exchanged between two communicating agents, but disregard the information
conveyed by these messages. Recent approaches such as HAPN [18] and BSPL [14]
1
The merchant himself committed to have the goods delivered in case of payment.

22
have started to consider also the information dimension. HAPN is formally based on
automata where nodes represent states of the interaction and transitions between nodes
represent the messages that can be exchanged. Transitions have a complex structure
since for each message it is possible to define a guard condition on message sending.
A similar approach is BSPL where the information flow is decomposed in a number of
“simple protocols”, each defining the schema of the messages that can be exchanged
together with their parameters. Parameter are decorated as in or out (meaning it is re-
ceived or emitted). BSPL provides a formal framework in which it is possible to verify
properties such as liveliness and safety of a protocol. Both HAPN and BSPL, how-
ever, show some weaknesses in properly handling information. In HAPN, for instance,
guards, that enable message sending, may refer to information which is not carried by
the message itself, but rather maintained in an external information system, which is
not an integral part of the HAPN proposal, and hence the complete verification of an
interaction is not actually achievable. BSPL, on the other hand, assumes a distributed
view of information. Each participant has its own knowledge base, and the progression
of the interaction makes the local knowledge bases evolve. The problem, in this case, is
that each participant has just a local view of the information lifecycle. Thus, an agent
cannot create expectations about the behaviors of other participants as a consequence
of the messages it sends. The approach we propose overcomes these limitations. Busi-
ness artifacts abstract an information system, and provide the environment in which
the agents, which are autonomous loci of control, interact. Both business artifacts and
agents are first-class components. The autonomy and flexibility of the agents are pre-
served and supported; moreover, it is possible to reason both on the evolution of the
business artifacts and on the interaction. This work can be extended along three main
lines of research. First of all, an explicit normative layer paves the way to formal verifi-
cation techniques for cross-organizational business processes. In this respect, the notion
of accountability is rapidly gaining importance since, when more organizations come
into play, it is even more important to trace back who is responsible for what. First
steps can be found in [4]. Another promising extension is to understand how agents
could plan the use of business artifacts for reaching their goals. An initial attempt to
use social commitments in planning has been discussed in [2], but business artifacts
are yet to be considered. Finally, the standardized lifecycle of commitments can be the
key for developing an agent programming methodology, similar to the one discussed
in [1]. The idea is to program agents so that they can properly tackle part of the events
that are generated in the business artifacts of their interest; specifically, the state transi-
tions that occur to commitments in which they are involved. To conclude, we mention
RAW-SYS [10], which enriches the prescriptive process model with data-awareness.
Although RAW-SYS looks similar to a (normative) business artifact, the objectives of
the two models are quite different. RAW-SYS is essentially a framework for verifying
business processes taking into account both the control- and the data-flows. A normative
business artifact, instead, aims at coordinating autonomous agents.

Acknowledgements This work was partially supported by the Accountable Trustwor-
thy Organizations and Systems (AThOS) project, funded by Università degli Studi di
Torino and Compagnia di San Paolo (CSP 2014).

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