=Paper=
{{Paper
|id=Vol-494/paper-11
|storemode=property
|title=Towards an Architecture for Self-regulating Agents: A Case Study in International Trade
|pdfUrl=https://ceur-ws.org/Vol-494/coinpaper2.pdf
|volume=Vol-494
|dblpUrl=https://dblp.org/rec/conf/mallow/BurgemeestreHT09
}}
==Towards an Architecture for Self-regulating Agents: A Case Study in International Trade==
https://ceur-ws.org/Vol-494/coinpaper2.pdf
1
Towards an architecture for self-regulating
agents: a case study in international trade
Brigitte Burgemeestre, Joris Hulstijn, and Yao-Hua Tan
- and open agent systems. An enforcement mechanism that
Abstract—Norm-enforcement models applied in human elaborates on an agent’s internal architecture to achieve
societies may serve as an inspiration for the design of multi-agent compliant behavior, and does not require additional
systems. Models for norm-enforcement in multi-agent systems ‘observers’ is self-regulation. Self-regulation is a control
often focus either on the intra- or inter-agent level. We propose a
combined approach to identify objectives for an architecture for
approach in which rule making and/ or enforcement are carried
self-regulating agents. In this paper we assess how changes on the out by the agent itself, instead of a regulator agent or
inter-agent level affect the intra-agent level and how a generic institution. It can be an alternative or extension to direct
BDI architecture IRMA can be adapted for self-regulation. The control, when external supervision and norm enforcement are
approach is validated with a case study of AEO certification, a not possible at all, are ineffective or when there is a lack of
European wide customs initiative to secure the supply chain while controlling resources. For example, in e-institutions it might be
facilitating international trade.
impossible to check all agent actions for compliance in real
Index Terms—self-regulation, agent architectures, norm time. A solution then might be to do a code review up
compliance forehand and determine if an agent is compliant by design. In
human societies programs of self–regulation have been found
I. INTRODUCTION to contribute to expanded control coverage and greater
inspectorial depth [2]. Self regulation can be implemented in
T o motivate autonomous agents to comply with norms
various enforcement mechanisms have been proposed.
Norms here define standards of behavior that are acceptable in
various ways: from voluntary self regulation, where a group of
agents voluntary chooses to regulate themselves, to mandated
a society, indicating desirable behaviors that should be carried or enforced self-regulation, where a government agency
out, as well as undesirable behaviors that should be avoided delegates some of its regulative and enforcing tasks to the
[8]. Enforcement mechanisms often require the introduction of agents subjected to the norm, but retains the supervision, to a
special “observers” or “regulator agents” that actively monitor combination of mandated self regulation and direct control by
the behavior of the other agents [1]. Such agents are assigned regulator agents [10]. Each model of self-regulation causes
to monitor the behavior of other agents and sanction them in different agent dependencies and information needs, which
case of norm violations. When developing norm enforcement imposes different requirements on the IT architecture.
mechanisms for multi-agent systems, the modeling is often A special case of self regulation for international trade is the
focused on the inter-agent level (between agents). Such models Authorized Economic Operator (AEO) program [7]. The AEO
aim to analyze agent interactions and dependencies to program is a European wide customs initiative that aims to
construct norm enforcement mechanisms. The intra-level secure the supply chain while at the same time reducing the
(inside the agent) is mainly treated as a black box. We argue administrative burden for companies through the use of self-
that the intra- and inter-agent aspects cannot be viewed control. Companies that are reliable in the context of customs
separately from each other, especially in norm enforcement related operations and have a good internal control system may
where perceptions of external stimuli should motivate an agent apply for the AEO certificate and receive operational benefits
to adapt its behavior and thereby its internal mechanisms. from simplified customs procedures, preferential treatment,
Norm-enforcement models applied in human societies may and less physical inspections. Companies that do not have an
serve as an inspiration for the design of electronic institutions AEO certificate remain subject to the current level of customs
controls. Participation in the AEO program is voluntary, but
effective self-control is an obligatory requirement.
Manuscript received June 26, 2009. This research was partially funded by Implementing self-regulation as a control mechanism thus
PGS IT Audit of the VU Amsterdam and the integrated project ITAIDE of
the 6th Framework of the IST Programme of the European Commission
results in a redistribution or delegation of control tasks among
All authors are with the faculty of Economics and Business Administration the actors. Agents have to adapt their internal mechanisms to
of the VU University, Amsterdam, The Netherlands. The third author is also cope with these tasks. We see that changes at the inter-agent
with the Dept. of Technology, Policy and Management of the Technical
level affect the intra-level. We therefore propose a combined
University Delft.
Brigitte Burgemeestre (e-mail: cburgemeestre@feweb.vu.nl). approach to develop an architecture to embed self-regulation
Joris Hulstijn (e-mail: jhulstijn@feweb.vu.nl). as a control mechanism for multi-agent systems.
Yao-Hua Tan (e-mail: ytan@feweb.vu.nl).
2
In this paper we present our first steps towards an norms for A. R uses information about A and A’s actions to
architecture for self-regulating agents. The research questions select the appropriate norms from the norm framework that
we like to answer in this paper are: 1. What objectives need to apply to A’s specific situation. R2 determines ‘control
be met by an architecture on self-regulating agents? 2. How do indicators’ of A. A ‘control indicator’ is the kind of evidence
we need to adapt existing Beliefs Desires Intentions (BDI) [9] required to demonstrate compliance of a norm, as well as
architectures? As a starting point we propose a combination of infrastructural requirements to collect that evidence. For
frameworks to cover the inter- as well as the intra-agent example: when a company sends an invoice, they always make
analysis. For the inter-agent analysis the Intelligent Resource- a copy of the invoice and store the copy to be able to check if
Bounded Machine Architecture (IRMA) [3] is a good starting the invoice payments are correct and complete. R3 is the
point because it is a general BDI architecture that is well monitoring performed by R on A’s actions, based on
accepted and has formed the basis for more recent agent information provided by A about the control indicators. R4
architectures. Software engineering methodology TROPOS describes the plan of R to sanction A in case of a norm
[6] provides suitable concepts to analyze and model agents’ violation. Agent A’s model is quite simple, as A is a ‘blind’
dependencies. We analyze direct regulation and self-regulation agent that has no knowledge about the norms or control
using TROPOS (Section II). Using this analysis we generalize indicators and only acts. Therefore it is possible that A
the objectives for the internal architecture of a self-regulating unknowingly engages in an activity that violates a norm that is
agent. We try to embed the normative objectives in IRMA imposed upon A by R. However, we do assume that A
(Section III). Using the extended architecture and TROPOS remembers action-sanction relations and that it can decide to
model, we analyze a case study of AEO (Section IV). We cancel an action that will lead to a sanction. Figure 1 shows the
examine if our adapted version of the architecture covers the dependency analysis for direct control.
findings of the case study. We identify its suitability and the
shortcomings.
II. INTER-AGENT ANALYSIS
We first analyze the agents and the dependencies among
agents. To do this we use concepts from the early requirements
phase of the TROPOS methodology [4], which is derived from
the i*conceptual framework[11]. The key concepts we use are:
actor, goal, plan, resource and dependency. An actor can be an
autonomous agent that has a goal or strategic interest. A goal
can be satisfied through the execution of a plan, which is an
abstract representation of a way of doing something. A
resource can be a physical or informational entity. Actors can
depend on each other to reach a certain goal, to execute a plan
or to obtain resources. The agent that depends on another
agent is called the depender, the agent he depends on is called
the dependee. The object which is the subject of the
dependency relation is called the dependum.
We first model the direct control approach where the actions
of autonomous agents are regulated by special regulator
agents. After that we analyze self-regulation and assess what Figure 1: TROPOS model of direct control. The actions of
changes when an autonomous agent internalizes control tasks an actor (A) are regulated by a regulator (R). Note that
of the regulator agent. arrows depict dependency, not information flow. So to
A. Agents’ dependencies in direct control regulate A’s actions, R depends on A for info about actor
and actions.
In direct control we have two types of agents: an Actor
agent (A) that is carrying out an activity and a Regulator agent B. Agents’ dependencies in self-regulation
(R) that is responsible for regulating A’s actions such that For self-regulation we start again with two types of agents:
agent A complies with the norms that are applicable to A. An the actor agent (A) and the regulator agent (R). In self-
agent can violate the norms through pursuing an illegal goal or regulation control tasks are delegated from R to A. Since A is
by performing an illegitimate action. We assume that R has a autonomous, R can never be absolutely certain that A
norm framework from which it derives the set of norms complies. R thus has to implement a mechanism to motivate A
tailored to an agent’s specific situation. To regulate A, agent R to regulate itself appropriately. Furthermore to maintain the
has to have the following plans: R1: Specify norms for actor, power of the regulator to handle non-compliant agents, the
R2 ‘Determine control indicators of actor’, R3 ‘Monitor sanctioning task (R4) remains the regulators responsibility.
actor’s actions’ and R4 ‘Sanction actor’. R1 generates a set of We first consider the consequences of the internalization of
3
control tasks by A. Plans R1, R2 and R3 may be internalized instead of its activities. In auditing R5 refers to a system-based
by agent A as plans: A1 ‘Specify norms’, A2 ‘Determine audit, were the focus is on the control system itself instead of
control indicators’ and A3 ‘Monitor actions’. A1 specifies the business transactions. Before an agent thus can enter in a
norms based on a norm framework which originates from R. self-regulative relation it has to provide for its authenticated
This entails a new dependency between A and R: A now control architecture or control script to the regulator. Figure 2
depends on R for communicating the norm framework. When shows the dependencies between agents A and R when they
the norm specification is done by A, A is also supposed to be engage in self-regulation. When we compare direct control
able to differentiate between norm violations and norm with self-regulation we see that A internalizes some of R’s
compliance. A therefore no longer depends for information control activities on A. New information resources have to be
about violations and permissions on R, but has to do it himself. gathered to be used within the control activities. Also new
A2 defines control indicators about A’s actions, based on the goals evolve and consequently the adoption of new plans. In
norms defined in A1. A3 describes the monitoring actions of A correspondence new dependencies between R and A develop
which it performs in the context of the control indicators from for the acquisition of other information resources
plan A2. The plans A1, A2, and A3 together, should support A Summarizing, a self-regulating agent has to have the
to act compliantly with the norms. The acts of A in return capabilities to: (1) Detect, internalize and store applicable
affect the nature of the control actions. If A starts doing norms in the environment, (2) Translate norms into measurable
different activities the control indicators may become less control indicators, and (3) ‘Monitor, detect and mitigate
effective and A therefore has to determine new control possible norm violations’. In the next section we zoom into the
indicators that cover the norms. For example, if A replaces the internal architecture of the actor agent in self-regulation
process of sending paper invoices to its customers by sending
them electronic invoices, new control indicators are required; III. INTER-AGENT ANALYSIS
e.g. log files instead of paper copies of the invoice. We now analyze how the new tasks and dependencies
revealed by the TROPOS models affect an agent’s internal
architecture. We acknowledge that these tasks are complex
normative tasks As a basis for our model we use the Intelligent
Resource-Bounded Machine Architecture (IRMA) [3]. The
architecture is a BDI architecture where the intentions are
structured into plans. A plan can be the plan that an agent has
actually adopted, or a plan-as-recipe that is stored into the plan
library. Plan options are proposed as a result of means-end
reasoning or by the opportunity analyzer. The opportunity
analyzer detects changes in the environment and determines
new opportunities, based on the agent’s desires. The options
are filtered through a compatibility filter, that checks the
options to determine compatibility with the agent’s existing
plans, and a filter override mechanism, in which the conditions
are defined under which (portions) of plans need to be
suspended and replaced by another option. The deliberation
process determines the best option on the basis of current
beliefs and desires.
Consider an autonomous agent that likes to achieve a certain
goal. The agent has already several plans of action available
(in its plan-library) to reach this goal. Before deliberating on a
plan, the agent engages in a filtering process. This process
Figure 2: TROPOS model of self-regulation, control tasks
constrains the agent’s possible plans, to plans that can be
of the regulator are internalized by the actor agent.
completed given its available (sub) plans in the plan library, its
Now we describe the consequences of A’s internalization of beliefs and desires. The agent chooses from this selection the
the control tasks of R’s goals and plans. Since A now has to best plan, given its beliefs and desires, and executes the plan.
control its own actions, the goal of R to regulate A’s actions is Figure 3 shows our extension of the IRMA architecture,
supposed to be met by the control activities of A. To determine adapted for self-regulation. Norm related adaptations are
if this delegation of control is effective, R’s has adopted a new shown in grey and dotted lines. The ovals in the figure are
goal which is to regulate the control activities of A. To reach information stores (repositories) and the rectangles are process
this goal, R also has defined a new plan (R5). R5 describes the modules.
activities of R to monitor and evaluate A’s control actions. R Within IRMA we like to implement the processes and
now depends on A for information about its control activities information stores that are needed for self-regulation. A self-
4
regulating agent needs to internalize certain control activities implementations make it possible for an agent to decide not to
to control its actions. The activities are: specify norms (A1), consider a plan option that aims at buying a snake skin
determine control indicators (A2), and monitor actions (A3). handbag. The opportunity analyzer may use the norms and
These control activities require input from the agent’s actions, beliefs to search for an alternative, such as a fake snake skin
and the actions in turn are influenced by the norms. We first handbag.
analyze what modules IRMA are possibly affected by We find that norms can impact all components of the
normative reasoning architecture. To assure consistent norm application we propose
a central information-storage for norms similar to what the
plan library is for plans. Activity A1 updates the norm library
according to the beliefs of the agent. Only norms that are
considered to be applicable to the agent’s specific situation are
included. To make an agent aware of a norm (violation) we
connect the norm library with the reasoner module that is
attached to the beliefs. If an agent then reasons about its
beliefs, it takes the norms into account. Beliefs about a norm
(violation) can be used as input for the means-end reasoner,
opportunity analyzer and the deliberation process. Besides
that, the agent may use its knowledge about norms to
determine the control indicators of A2. We consider the
filtering process the best location to implement the control
indicators. Beliefs about norms are already included in the
other reasoning processes. The filtering process and reasoning
thus together consider (non-) compliant behavior. We think
that the majority of the control indicators should be embedded
in the compatibility filter and only severe violations should be
handled by the filter override mechanism. Otherwise it could
happen that the filtering is too strict. The monitoring in A3 is
handled through a comparison of the beliefs about the data on
the indicators with the norms. Based on results from this
Figure 3: A reasoning component for self-regulating agents
adapted from [3] analysis controls in the filtering process may be adapted.
Norms can impact the information stores and or processes Figure 3 shows an adapted version of the rational agent
of the architecture. A norm can be implemented in plans and reasoning architecture for self-regulation.
function as a threshold to restrict the outcome. For example, a Our approach of embedding norms into the filtering process
thermostat function that tries to keep the room heated at a is compatible with the framework that is proposed by [8].
certain temperature. Norms can also restrict the possible set of Norms can also be implemented into the goal generation
plans. Plans that violate the norm are not stored in the plan mechanism as was done in the BOID architecture [5]. In
library. Or in means-end reasoning: there are illegal plans BOID one can distinguish two kinds of goals: internal
available in the plan library but we do not consider them as motivations (desires), representing individual wants or needs,
appropriate options to reach a goal. Norms can also prevent and external motivations (obligations) to model social
the actual execution of a plan. For example, a person can plan commitments and norms[5]. All these potential goals may
to rob a bank, but decide not to do so. conflict with each other. To resolve conflicts among the sets of
Besides that, norms affect the beliefs, and beliefs affect the beliefs, obligations, intentions and desires, a priority order is
norms. An agent may realize, based on its beliefs, that it is needed. In the BOID, such a (partial) ordering is provided by
acting non-compliant with the norms. Or, an agent realizes that the agent type.
due to a change in activities certain norms are no longer
applicable and new norms must be incorporated. When an IV. CASE STUDY AEO CERTIFICATION
agent adopts a new norm, this must be known (believed). We use our models to analyze a specific case of self-
Norms are also related to the desires of an agent. An agent’s regulation: AEO certification. The case study results are based
desires may violate the norms. For example, an agent may on document analysis and a series of semi-structured
desire a handbag that is made of the skin of a protected snake. interviews with experts from Dutch Tax and Customs
A norm is that killing a protected animal is illegal. If norms are Administration, held in the period of May till November 2009.
included in the compatibility filter, an agent can check if an Meeting notes were made by the authors and verified by
option is compatible with its norms. If norms are part of the interview partners. Intermediate results of the case study were
filter override mechanism, non-compliance can be a condition validated in a one-day workshop.
under which an agent always has to reconsider its plans. Both An Authorized Economic Operator (AEO) can be defined as
5
a company that is in-control of its own business processes, and if the likelihood of a threat is limited and the risk is partially
hence is reliable throughout the EU in the context of its covered, or if the costs for complete coverage are very high.
customs related operations [7]. Typically, modern enterprise The company has to motivate its choices in its system of
information systems (e.g ERP, CRM etc.) play an essential control measures to customs. It has to show how its risk
role for companies to be in-control. AEO’s will receive several management approach contributes to being a self-controlling
benefits in customs handling, such as a “Green Lane” and reliable party. The company therefore evaluates the
treatment with a reduced number of inspections. These effective implementation of the proposed measures, using the
benefits can lead to considerable cost-reductions for COSO internal control scoring definitions. COSO is a
businesses. For non-certified enterprises customs will continue framework for risk management and internal control [12]. The
to carry out the traditional supervision. Customs can thus scores range from 0 “no control measures in place”, 1 “internal
direct their efforts towards non-certified companies to increase control is ad hoc and unorganized”, 2 “internal control has a
the security of international supply chains, while at the same structured approach”, 3 “internal control is documented and
time reducing the administrative burden for AEOs. known”, 4 “internal control is subject to internal audits and
To qualify as AEO, a company must meet a number of evaluation” until 5 “internal control measures are integrated
criteria, which are described in the community customs code into the business processes and continuously evaluated”. This
and the AEO guidelines [7], which are developed by the scoring provides the customs with an indication of the maturity
European Commission. Part of the application procedure is a level of the company’s self-controlling abilities.
self-assessment on the quality of the company’s internal
B. Case analysis
control system for aspects that are relevant to the type of AEO
certificate (‘Customs simplifications’, ‘Security and safety’ or In the AEO case study we see the implementations of tasks
‘Combined’ [7]). The company’s approach and the results of A1, A2, and A3 at the company’s side. A company has to
the self-assessment are inspected by customs. The customs define a control system appropriate to handle its specific risks.
determine whether the self-assessment is performed well and The company therefore translates the general AEO guidelines
whether the results indicate that a company is able to control into norms that are applicable in its own practice and
its business processes such that they contribute to a secure circumstances (compatible with A1). Thereby a company
supply chain. If this is the case and the other requirements are determines parameters to control its business processes (A2).
met an AEO certificate is issued by the customs office. Next A company with a control system of a high maturity level
we focus on the self-assessment task. monitors its actions (A3) through internal audits and controls
that are integrated in the processes. The customs replaces its
A. The self-assessment task traditional controls of the company’s processes (R1, R2, R3)
The company’s first task is to collect information related to by an assessment of the company’s self-regulating capabilities
the specific nature of the company to focus the self- and monitors the control actions of the company (R5). We also
assessment. This step is called ‘Understanding the business’. observe dependencies on information needs. The company
The next step is to identify (potential) risks to which the depends on abstract norms (e.g. the AEO Guidelines) provided
business is exposed using the AEO guidelines, which provide by the customs, which they try to apply to themselves as
an overview of general risk and attention points. The company customs would do. The customs on the other hand depends on
determines which sections are important according to the the company for information about their control system.
nature of the business activities. A company then has to The AEO case provides us a new approach of control that
identify, what risks affect the supply chain’s safety, and are could be applied to a multi agent system. It shows that norm
therefore of interest of the customs authorities. The company enforcement can be a task that can be distributed between
thus replaces the customs’ task of risk identification. For various types of agents. Furthermore we learned that self-
example, computer components are valuable goods, which are regulation only works under certain conditions and that
subject to theft. Trading valuable goods requires more security delegating control tasks is not simple. In general companies
measures, than, say, trading in a mass product like fertilizer. find it difficult to do a self-assessment as they do not know
However, some ingredients of fertilizer may be used to what customs expects from them. Especially the specification
assemble explosives, leading to a different set of risks of abstract norms of the AEO guidelines into company specific
A company then assess if appropriate internal control concrete norms proved to be hard. For companies it is thus
measures are taken to mitigate these risks. The vulnerability of unclear when they have taken sufficient measures to secure
a company to threats depends on its current control measures. their part of the supply chain. Companies expect from the
Control measures either reduce the likelihood, by dealing with customs to indicate on a more detailed level what is sufficient:
vulnerabilities (preventative controls), or reduce the impact “A fence for a chemical company should be X meters high”.
(detective and corrective controls). A robust system of controls Even for customs such knowledge is often only implicitly
is thus able to prevent, detect and correct threats. A robust available as “expert knowledge” that is difficult to externalize
system of controls should also monitor its own functioning. and make accessible for companies.
For risks that are not controlled, additional measures may be When we look at the company’s internal control system we
implemented or the risk is “accepted”. Risks can be accepted, see that norms have to be internalized based on perceptions of
6
the environment. Only applicable norms are implemented. The identify objectives for an architecture for self-regulation. We
norms have to be implemented in a systematic and structured identified key processes and their influence on the
way such that they detect norm violations and prevent them dependencies between agents and the internal agent
from occurring. In the architecture we see norms implemented architecture. The models provide insight in differences in
as a filtering mechanism. In the AEO certification we see norm requirements for direct controlled agents and self-regulating
control as a structured process. In addition, mature self- agents. The analysis also points out the limitations of some
controlling companies may have controls integrated in the well-known existing approaches. IRMA lacked in reflective
processes or audits to check the functioning of the controls. capabilities and is therefore not sufficient to model a truly self-
The total control system of a company could be seen as their regulating agent: an agent that is able to learn from its
implementation of the internal control architecture. Therefore experiences with norms and use these experiences as
these new monitoring activities of customs in the AEO case constraints for future normative reasoning. Also unaddressed
could be seen as quality assessment of such a control were aspects of norm communication. For two agents to
architecture rather than the traditional role of Customs to engage in a self-regulation relation, they must able to
control the specific business operations of the company. This communicate the norms effectively. Since the agents are
fundamental change in the controlling role of the government autonomous we cannot simply assume that both agents use
is often referred to as the transformation from operational similar vocabularies or protocols [6]. A solution for norm
control to meta-control, where operational control is delegated communication should take the agent’s autonomy into account.
by the Customs to the companies themselves. Future research will zoom in on the role of reflection on
normative behavior and the communication of norms. Besides
V. DISCUSSION that we are also interested in the evolution process of an agent
The combination of TROPOS and IRMA for self-regulating from direct control to self-regulation.
agents also has its limitations. However, we do not claim that
these are the best approaches currently available. Instead we Acknowledgments We would like to thank the Dutch Tax and
used the approaches as a means to identify requirements for Customs administration for their discussions.
self-regulating agent at the intra- and inter-agent level. Below
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