MONITORING COOPERATIVE BUSINESS CONTRACTS IN AN

INSTITUTIONAL ENVIRONMENT

Henrique Lopes Cardoso and Eug

enio Oliveira

LIACC, DEI / Faculdade de Engenharia, Universidade do Porto, R. Dr. Roberto Frias, 4200-465 Porto, Portugal

Keywords:

Electronic contract monitoring, Contractual obligations, Deadlines, Rules.

Abstract:

The automation of B2B processes is currently a hot research topic. In particular, multi-agent systems have been

used to address this arena, where agents can represent enterprises in an interaction environment, automating

tasks such as contract negotiation and enactment. Contract monitoring tools are becoming more important

as the level of automation of business relationships increase. When business is seen as a joint activity that

aims at pursuing a common goal, the successful execution of the contract beneﬁts all involved parties, and

thus each of them should try to facilitate the compliance of their partners. Taking into account these concerns

and inspecting international legislation over trade procedures, in this paper we present an approach to model

contractual obligations: obligations are directed from bearers to counterparties and have ﬂexible deadlines.

We formalize the semantics of such obligations using temporal logic, and we provide rules that allow for

monitoring them. The proposed implementation is based on a rule-based forward chaining production system.

1 INTRODUCTION

Technological support for B2B is increasing. A line

of research consists on automating (part of) the pro-

cess of creation and execution of e-contracts, through

multi-agent systems (MAS) technology: software

agents can be used as enterprise delegates, automat-

ing tasks such as contract negotiation and enactment.

We have formalized and developed an Electronic

Institution platform motivated by the need to develop

services that assist agents (representing real-world en-

tities) when interacting with the aim of establishing

business relationships. Contracts make the commit-

ments of parties explicit, and an institutional environ-

ment seeks to monitor and enforce those contracts.

In the B2B world (particularly in cases where a

business relationship is strategic) it is often the case

that parties cooperate in contract enactment. A con-

tract speciﬁes obligations between contractual parties,

and provides legal options handling non-compliance

cases. When a business relationship is seen as a joint

activity that aims at pursuing a common goal, the suc-

cessful execution of the contract beneﬁts all involved

parties, and thus each of them should try to facilitate

the compliance of their partners. In an agent-based

environment for contract monitoring, each party is

represented in the system by a software agent. Au-

tomated monitoring tools should take into account

that agents may be cooperative enough to allow, in

some circumstances, deviations from their counter-

parties. This is because group success also beneﬁts

each agent’s private goals, which are not limited to the

ongoing business relationship, but also concern future

opportunities that may arise.

Our approach to contract monitoring is based on

real-world evidence from business contract legisla-

tions, namely the United Nations Convention on Con-

tracts for the International Sale of Goods – CISG

(UNCITRAL, 1980), which denotes a ﬂexible and

even cooperative facet of trade contracts. In this paper

we present the model of contractual obligations that

we advocate – directed obligations with liveline and

deadline. We formalize the semantics of these obliga-

tions and provide an implementation concerning the

development of an institutional contract monitoring

environment.

The paper is structured as follows. In section 2

we introduce our approach to model contractual obli-

gations, and formalize their semantics using temporal

logic. In section 3 we translate this formalization to

a rule-based approach, and we provide an implemen-

tation using a rule engine. An example is given that

shows how a model of ﬂexible deadlines can be ex-

ploited in a contract. Section 4 concludes.

206

Lopes Cardoso H. and Oliveira E. (2009).

MONITORING COOPERATIVE BUSINESS CONTRACTS IN AN INSTITUTIONAL ENVIRONMENT.

In Proceedings of the 11th International Conference on Enterprise Information Systems - Artiﬁcial Intelligence and Decision Support Systems, pages

206-211

DOI: 10.5220/0002005302060211

 SciTePress

2 MODEL FOR CONTRACTUAL

OBLIGATIONS

When reaching a business agreement, parties sign a

contract including norms that describe their commit-

ments. In our approach, an institutional normative

environment provides a contract monitoring service,

including contractual norms and monitoring rules.

Active monitoring requires recording contract-related

events in a so-called normative state, including:

• institutional facts: institutionally recognized facts

that are brought about by agents

• obligations: what agents should do

• fulﬁllments: obligations that are fulﬁlled

• violations: obligations that are not fulﬁlled

Elements other than institutional facts are envi-

ronment events, asserted in the process of norm ac-

tivation and monitoring. Obligations describe what

agents should do, and may be fulﬁlled or not. We

consider different violation states, as explained later.

In general terms, a norm prescribes, given a cer-

tain state of affairs, what an agent is obliged to do:

situation → prescription. The situation comprises

any combination of events that have occurred for a

given contract. Consider, for instance, a simple pur-

chase contract. We may have a norm indicating that

when the seller issues the invoice (modeled as an in-

stitutional fact), the buyer is obliged to pay. Further-

more, we may state that once the payment has been

fulﬁlled, the seller is obliged to send the receipt. We

may also deﬁne norms based on violations: if the

buyer does not pay within due date, an interest rate

will be applied.

2.1 Directed Obligations with Time

Windows

When specifying norms in contracts, deadline han-

dling is central to deﬁne the semantics of contractual

obligations

. We have developed a model for these

obligations inspired in the CISG convention (UNCI-

TRAL, 1980). Consider the following excerpts:

Article 48: (1) [...] the seller may, even after the

date for delivery, remedy at his own expense any

failure to perform his obligations, if he can do so

without unreasonable delay [...]; (2) If the seller

requests the buyer to make known whether he will

Deontic operators include also permissions and prohi-

bitions, but we ﬁnd obligations to be particularly relevant in

the domain of business contracts.

accept performance and the buyer does not com-

ply with the request within a reasonable time, the

seller may perform within the time indicated in his

request. [...]

Article 47: (1) The buyer may ﬁx an additional pe-

riod of time of reasonable length for performance

by the seller of his obligations.

Article 63: (1) The seller may ﬁx an additional pe-

riod of time of reasonable length for performance

by the buyer of his obligations.

Article 52: (1) If the seller delivers the goods before

the date ﬁxed, the buyer may take delivery or refuse

to take delivery.

From this source we can see that even though a

deadline has been violated, the bearer may still be en-

titled to fulﬁll his obligation (Art. 48). Also, the coun-

terparty of an obligation may concede an extended

deadline to the bearer (Art. 47 and 63). Finally, antic-

ipated fulﬁllments are not always welcome (Art. 52).

Based on this background, we deﬁne the notion

of directed obligation with liveline and deadline –

b,c

( f , l, d): agent b is obliged towards agent c to

bring about f between l (a liveline) and d (a dead-

line). Directed obligations have been proposed in

(Herrestad and Krogh, 1995): obligations are seen

as directed from a bearer (responsible for fulﬁlling

the obligation) to a counterparty. We interpret obli-

gations of this kind as claims from counterparties to

bearers (as in (Tan and Thoen, 1998)): if b does not

bring about f between l and d then c is authorized to

react against b. Note that this reaction is discretionary,

not mandatory.

The temporal boundaries of obligations deﬁne a

time window within which the obliged fact should oc-

cur. However, not all obligations need livelines. In

fact, Art. 52 above is important only in the context of

trade transactions, where storage costs may need to be

considered. If, on the contrary, a deadline is enough,

we can ﬁx l to the moment when the obligation arises.

We avoid using a ﬁxed time reference for obligation

fulﬁllment (an obligation for bringing about f at time

t), which is suggested in Art. 52; we ﬁnd it more con-

venient to deﬁne a ﬁxed date as an interval, say, from

the start till the end of a speciﬁc date.

In order to accommodate the cooperative nature of

contract execution, the semantics of directed obliga-

tions with liveline and deadline is interdependent with

the notion of authorization. We consider that if fact f

is not yet the case when deadline d arises, the obli-

gation is not yet violated, but is in a state where the

counterparty is authorized to react. We emphasize the

case for a deadline violation (as opposed to obligation

violation). This comprises a ﬂexible approach to han-

MONITORING COOPERATIVE BUSINESS CONTRACTS IN AN INSTITUTIONAL ENVIRONMENT

207

Figure 1: Lifecycle of a directed obligation with liveline

and deadline.

dling non-ideal situations: each deadline violation is

different, as each may have a different impact on the

ongoing business, and occurs between a speciﬁc pair

of agents with a unique trust relationship.

A deadline is meant to indicate when the counter-

party is authorized to react to the non-fulﬁllment of

an obligation directed to him. A possible reaction is to

declare the obligation as violated. However, the coun-

terparty might want to concede an extended dead-

line, by waiting further for the obligation to be ac-

complished. We introduce the element Den

c,b

(f , l, d),

which is a denounce from agent c towards agent b re-

garding the failure of the latter to comply with his

obligation to bring about f before d. We consider

the achievement of facts to be common knowledge:

a party may only denounce the non-fulﬁllment of an

obligation while that obligation is not fulﬁlled yet.

Figure 1 illustrates the lifecycle of a directed obli-

gation with liveline and deadline: O

b,c

( f , l, d). When

l arises the obligation becomes pending, since only

then it may be fulﬁlled according to the terms of the

contract. In case of an anticipated achievement of f

(a liveline violation), we only need l to consider the

obligation as fulﬁlled. However, this does not prevent

the counterparty from reacting to this early achieve-

ment (although not by denouncing it). A deadline vi-

olation can be resolved in two ways: successfully by

achieving f , or unsuccessfully by a denounce.

2.2 Formalization

We deﬁne the following terms to signal the occur-

rence of speciﬁc obligation states:

• obligation O

b,c

( f , l, d): the obligation was pre-

scribed by a norm, and is therefore active

• liveline violation LViol

b,c

(f , l, d): the fact being

obliged has been brought ahead of time

• deadline violation DViol

b,c

(f , l, d): the fact being

obliged should have been brought already

• fulﬁllment Fulf

b,c

(f , l, d): the obligation was ful-

ﬁlled

• violation Viol

b,c

(f , l, d): the obligation was vio-

lated and cannot be fulﬁlled anymore

Borrowing from temporal logic

, the following re-

lations express the semantics of our obligations:

• O

b,c

(f , l, d) ∧ (f B l) |= LViol

b,c

(f , l, d)

Lifeline violation occurs when the obliged fact is

brought about before the liveline.

• O

b,c

(f , l, d) ∧ l ∧ (f B d) |= Fulf

b,c

(f , l, d)

Fulﬁllment occurs after the liveline and when the

obliged fact is brought about before the deadline.

• O

b,c

(f , l, d) ∧ (d B f ) |= DViol

b,c

(f , l, d)

Deadline violation occurs when the deadline

comes before the obliged fact.

• DViol

b,c

(f , l, d) ∧ (f B Den

b,c

(f , l, d))

|= Fulf

b,c

(f , l, d)

Fulﬁllment occurs after a deadline violation if the

obliged fact is obtained before a denounce.

• DViol

b,c

(f , l, d) ∧ (Den

c,b

(f , l, d) B f )

|= Viol

b,c

(f , l, d)

Violation occurs after a deadline violation if a de-

nounce is made before the obliged fact occurs.

We have two kinds of temporal violations: live-

line violations – LViol

b,c

(f , l, d) – and deadline viola-

tions – DViol

b,c

(f , l, d). Note that when the deadline is

reached we set the obligation to have a violated dead-

line, but not to be violated in itself. Only a denounce

establishes such a state.

3 AUTOMATED MONITORING

RULE-BASED ENGINE

The logical relationships expressed above provide us

a formalism to deﬁne directed obligations with live-

lines and deadlines. In order to develop appropriate

tools to monitor contracts at run-time, we ground this

semantics into a reasoning engine capable of respond-

ing to events as soon as they occur. A natural choice

we have made before (Lopes Cardoso and Oliveira,

2009) is the use of a rule-based forward-chaining pro-

duction system. These systems are composed of a

knowledge base (rules), a working memory (facts)

and an inference engine that matches rules’ conditions

with facts, producing changes in working memory.

Figure 2 instantiates these concepts to our purposes.

We will follow linear temporal logic (LTL) (Emerson,

1990), with a discrete time model. Let x = (s

, s

, ...)

be a timeline, deﬁned as a sequence of states s

. The syntax

x |= p reads that p is true in timeline x. We write x

to denote

state s

of x, and x

|= p to mean that p is true at state x

. We

use a weak version of the before LTL operator B, where q is

not mandatory: x |= (p B q) iff ∃

|= p ∧ ∀

k<j

|= ¬q)).

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208

Figure 2: Monitoring as a rule-based production system.

The following (forward-chaining) rules can be de-

ﬁned to implement the semantics of directed obliga-

tions with livelines and deadlines:

• O

b,c

(f , l, d) ∧ f ∧ ¬l → LViol

b,c

(f , l, d)

• O

b,c

(f , l, d) ∧ l ∧ f ∧ ¬d → Fulf

b,c

(f , l, d)

• O

b,c

(f , l, d) ∧ d ∧ ¬f → DViol

b,c

(f , l, d)

• DViol

b,c

(f , l, d) ∧ f ∧ ¬Den

b,c

(f , l, d)

→ Fulf

b,c

(f , l, d)

• DViol

b,c

(f , l, d) ∧ Den

c,b

(f , l, d) ∧ ¬f

→ Viol

b,c

(f , l, d)

Each relation of the form (e

B e

) was translated

into a conjunction e

∧ ¬e

. This allows us to detect

the moment at which the before relation holds, and

consequently to reason about its consequences. We

assume an immediate assertion of facts and temporal

references when they come into being. Furthermore,

rules are expected to be evaluated in every working

memory update (e.g. right after a fact is asserted),

in order to produce the indicated conclusions. These

conclusions are added to the normative state.

3.1 Reasoning with Time

In business contracts deadlines are usually depen-

dent on the fulﬁllment dates of other obligations. In-

stead of having ﬁxed (absolute) dates, these may at

times be relative, calculated according to other events.

CISG (UNCITRAL, 1980) expresses this by saying

that dates can be determinable from the contract:

Article 33: The seller must deliver the goods: (a) if

a date is ﬁxed by or determinable from the contract,

on that date; (b) if a period of time is ﬁxed by or

determinable from the contract, at any time within

that period [...]

Article 59: The buyer must pay the price on the date

ﬁxed by or determinable from the contract [...]

It is therefore useful to timestamp each event.

Therefore, Fulf

b,c

(f , l, d)

will indicate a fulﬁllment at

time point t; similarly for Viol

b,c

(f , l, d)

. Since a fact

itself has now a timestamp attribute, for ease of read-

ing we will write fact f achieved at time point t as

Fact(f )

. A denounce will be written Den

b,c

(f , l, d)

Norms will be based on these elements

and time references in order to prescribe obli-

gations with relative deadlines. For example,

Fulf

b,c

(Deliver(x), , )

→ O

c,b

(Pay(price), t, t + 10)

would mean that once agent b has fulﬁlled his

obligation to deliver x to agent c, the latter is obliged

to pay to the former within a period of 10 time units.

Having timestamps also allows us to deﬁne rules

with a closer reading to the LTL before operator:

• O

b,c

(f , l, d) ∧ Fact(f )

∧ t < l → LViol

b,c

(f , l, d)

• O

b,c

(f , l, d) ∧ l ∧ Fact(f )

∧ t < d → Fulf

b,c

(f , l, d)

• O

b,c

(f , l, d) ∧ d ∧ ¬(Fact(f )

∧ t < d)

→ DViol

b,c

(f , l, d)

• DViol

b,c

(f , l, d) ∧ Fact(f )

∧

¬(Den

c,b

(f , l, d)

∧ u < t) → Fulf

b,c

(f , l, d)

• DViol

b,c

(f , l, d) ∧ Den

c,b

(f , l, d)

∧

¬(Fact(f )

∧ t < u) → Viol

b,c

(f , l, d)

This kind of approach has the beneﬁt of relaxing

the rule evaluation policy, because we are checking

the timestamps of each event (see also (Lopes Car-

doso and Oliveira, 2009)).

3.2 Implementation with Jess

We have chosen Jess

(Friedman-Hill, 2003) to im-

plement our norm monitoring system. Jess is a very

efﬁcient rule engine based on the Rete algorithm for

pattern matching. We start by deﬁning appropriate

templates (through deftemplate constructs) for each

type of element in working memory. Jess facts follow

a frame-like notation, in which each fact has associ-

ated slots to be ﬁlled in. We have:

(deftemplate ifact

(multislot fact) (slot when) )

(deftemplate time

(slot when) )

(deftemplate obligation

(slot bearer) (slot counterparty)

(multislot fact)

(slot liveline) (slot deadline) )

(deftemplate liveline-violation

(slot obl) )

(deftemplate deadline-violation

(slot obl) )

The code presented in this section includes some sim-

pliﬁcations in order to make it more simple to understand.

MONITORING COOPERATIVE BUSINESS CONTRACTS IN AN INSTITUTIONAL ENVIRONMENT

209

(deftemplate fulfillment

(slot obl) (slot when) )

(deftemplate violation

(slot obl) (slot when) )

(deftemplate denounce

(slot obl) (slot when) )

For simpliﬁcation, we included a reference to the

obligation inside other templates. The time template

is used to assert the occurrence of time events (asso-

ciated with livelines and deadlines), which is done by

scheduling alerts using a system clock.

Implementing the monitoring rules in Jess is

straightforward. A Jess rule is written in the form

LHS => RHS, where LHS includes fact patterns that

will be matched against facts in working memory.

The RHS indicates actions to execute (such as assert-

ing new facts) when the rule is ﬁred. The following

rules (deﬁned with defrule constructs) translate di-

rectly from the monitoring rules shown above (identi-

ﬁers starting with a question mark are variables).

(defrule detect-liveline-violation

?obl <- (obligation (fact $?f) (liveline ?l))

(ifact (fact $?f) {when < ?l})

(assert (liveline-violation (obl ?obl))) )

(defrule detect-fulfillment

?obl <- (obligation (fact $?f) (liveline ?l)

(deadline ?d))

(time (when ?l))

(ifact (fact $?f) (when ?t))

(test (<= ?t ?d))

(assert (fulfillment (obl ?obl) (when ?t))) )

(defrule detect-deadline-violation

?obl <- (obligation (fact $?f) (deadline ?d))

(time (when ?d))

(not (ifact (fact $?f) {when <= ?d}))

(assert (deadline-violation (obl ?obl))) )

(defrule detect-belated-fulfillment

(deadline-violation (obl ?obl))

?obl <- (obligation (fact $?f))

(ifact (fact $?f) (when ?t))

(not (denounce (obl ?obl) {when <= ?t}))

(assert (fulfillment (obl ?obl) (when ?t))) )

(defrule detect-violation

(deadline-violation (obl ?obl))

?obl <- (obligation (fact $?f))

(denounce (obl ?obl) (when ?u))

(not (ifact (fact $?f) {when <= ?u}))

(assert (violation (obl ?obl) (when ?u))) )

These rules enable us to monitor the compliance

of agents with prescribed obligations. As explained

at the beginning of section 2, norms have a rule-like

format and are based on contract-related events, such

as those obtained by ﬁring monitoring rules. Not sur-

prisingly, norms too are implemented in Jess as rules.

What makes them norms is that typically they will be

used to obtain new obligations (these will be asserted

in the RHS of norms), which will then be monitored.

The normative environment’s monitoring capabil-

ities may be used as a tool for alerting agents when

certain contract-related events occur. Further rules

may be deﬁned with such a purpose. The RHS of Jess

rules may include function calls that implement the

desired level of responsiveness of the normative envi-

ronment in which notiﬁcations are concerned.

3.3 Example

In this section we show a simple example where the

concept of ﬂexible deadlines is exploited in an elec-

tronically supervised business relationship. We have

a contract between two agents, say B and S, wherein

S commits to supply, whenever ordered, good X for

7.5 per unit. The norms below (implemented as Jess

rules) deﬁne the contractual relationship and are in-

cluded in the institutional normative environment for

monitoring purposes. Agent S is supposed to deliver

the ordered goods between 3 to 5 days after the order

(norm n1), and agent B shall pay within 30 days (norm

n2). Furthermore, if agent B does not pay in due time,

he will incur in a penalty consisting of an obligation

to pay an extra 10% on the order total (norm n3). Fi-

nally, if agent S violates his obligation to deliver, the

contract will be canceled (n4).

(defrule n1

(ifact (fact order item X quantity ?q)

(when ?w)))

(assert

(obligation (bearer S) (counterparty B)

(fact delivery X qt ?q)

(liveline (+ ?w 3))

(deadline (+ ?w 5)) ) ) )

(defrule n2

(fulfillment (obl ?obl) (when ?w))

?obl <- (obligation (fact delivery X qt ?q))

(assert

(obligation (bearer B) (counterparty S)

(fact payment (* ?q 7.5))

(liveline ?w)

(deadline (+ ?w 30)) ) ) )

(defrule n3

(deadline-violation (obl ?obl))

?obl <- (obligation (fact payment ?p)

(deadline ?d))

(assert

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210

(obligation (bearer B) (counterparty S)

(fact payment (* ?p 0.10))

(liveline ?d)

(deadline (+ ?d 30)) ) ) )

(defrule n4

(violation (obl ?obl))

?obl <- (obligation (fact delivery X qt ?q))

(cancel-contract ID123) )

Note that the interest applied on payments is auto-

matic once a deadline violation is detected (norm n3).

On the other hand, a contract cancellation (norm n4)

requires that agent B denounces the inability of agent

S to fulﬁll the delivery. It is therefore up to agent B

whether to wait further and accept a delayed deliv-

ery or not. If the agreed upon contract conditions are

important enough, allowing a counterparty deviation

(and hence taking a cooperative attitude regarding the

compliance of the contract) may be a good decision.

Different kinds of situations may be easily mod-

eled using this kind of norms. Moreover, using ﬂex-

ible deadlines also ensures a degree of freedom for

agents to make decisions in the execution phase of

contracts, which is important for dealing with busi-

ness uncertainty.

4 CONCLUSIONS

In B2B relationships contracts specify, through obli-

gations, the interactions between different partners,

and provide legal options to which parties can re-

sort in case of conﬂict. However, when this joint ac-

tivity aims at pursuing a common goal, the success-

ful performance of business beneﬁts all involved par-

ties. Therefore, when developing automated moni-

toring tools, one should take into account that agents

may be cooperative enough to allow some counterpar-

ties’ deviations.

In this paper we have introduced a model for con-

tractual obligations. We deﬁned them as directed obli-

gations with livelines and deadlines. The directed as-

pect concerns the need to identify the agent who will

be authorized to react in case of non-fulﬁllment. Obli-

gation violations are now dependent on the counter-

party motivation to claim them.

Our approach is based on real-world evidence

from business contracts (namely the United Nations

Convention on Contracts for the International Sale of

Goods), which denotes a ﬂexible and even coopera-

tive facet of trade contracts. This extends to the con-

cept of B2B Virtual Organizations, wherein different

parties come together to share a business goal that

is achievable through the cooperative fulﬁllment of a

common contract.

Rule-based production systems allow applying

rules in a forward-chaining way. This data-driven ap-

proach is appropriate to model a norm monitoring en-

vironment based on events. Jess is a powerful rule en-

gine that allows a straightforward implementation of

our monitoring model. The easy integration of a Jess

engine with a Java application enables the develop-

ment of a full monitoring tool that aims at supporting

a cooperative model of business contracts enactment.

We envisage to apply this framework to more com-

plex business scenarios.

ACKNOWLEDGEMENTS

The ﬁrst author is supported by FCT (Fundac¸

para a Ci

encia e a Tecnologia) under grant

SFRH/BD/29773/2006.

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