=Paper=
{{Paper
|id=Vol-482/paper-2
|storemode=property
|title=From Real-World Regulations to Concrete Norms for Software Agents: A Case-Based Reasoning Approach
|pdfUrl=https://ceur-ws.org/Vol-482/Paper_2.pdf
|volume=Vol-482
|authors=Tina Balke,Paulo Novais,Francisco Andrade,Torsten Eymann
|dblpUrl=https://dblp.org/rec/conf/icail/BalkeN0E09
}}
==From Real-World Regulations to Concrete Norms for Software Agents: A Case-Based Reasoning Approach==
https://ceur-ws.org/Vol-482/Paper_2.pdf
From Real-World Regulations to Concrete Norms for
Software Agents – A Case-Based Reasoning Approach
Tina Balke1, Paulo Novais2, Francisco Andrade3, Torsten Eymann4
1
University of Bayreuth, Chair of Information Systems Management
Bayreuth, Germany, tina.balke@uni-bayreuth.de
2
Universidade do Minho, DI-CCTC, Braga, Portugal,
pjon@di.uminho.pt
3
Universidade do Minho, Escola de Direito,
Braga, Portugal, fandrade@direito.uminho.pt
4
University of Bayreuth Chair of Information Systems Management
Bayreuth, Germany, torsten.eymann@uni-bayreuth.de
Abstract. When trying to use software agents (SAs) for real-world business
and thereby putting them in a situation to operate under real-world laws, the
abstractness of human regulations often poses severe problems. Thus, human
regulations are written in a very abstract way, making them open to a wide
range of interpretations and applicable for several scenarios as well as stable
over a longer period of time. However, in order to be applicable for SAs,
regulations need to be precise and unambiguous. This paper presents a case-
based reasoning approach in order to bridge the gap between abstract human
regulations and the concrete regulations needed for SAs, by developing and
using a knowledge base that can be used for drawing analogies and thereby
serves as reference for "translating" abstract terms in human regulations.
Keywords: Software Agents, Case-Based Reasoning, Electronic
Contracting, Dispute Resolution
1. Introduction
Intelligent inter-systemic electronic contracting is a specific way of forming contracts
by electronic means in such a way that contracts are concluded and perfected
exclusively by the actuation and interaction of intelligent and autonomous informatics
devices capable of autonomous, reactive and proactive behavior, of reasoning, of
learning through experiences, of modifying their own instructions and, last but not
least, of making decisions on their own and on behalf of others (AI and Law) [35]. In
this form of contracting, an important role is played by intelligent software agents
(SAs). And these may be fictioned as tools controlled by humans or faced as subjects
of electronic commerce, they may be seen as legal objects or as legal subjects [4, 5].
Yet, in any case, it is important to legally consider their own and autonomous will
[6]. Thus, within the last years the vision of autonomous software agents conducting
inter-systemic electronic contracts on behalf of their principals in the Internet has
gained wide popularity and scientists have published a wide number of papers with
possible application scenarios [24]. However, when thinking about these scenarios
one needs to keep in mind, that the Internet (as an extension of the real-word) and all
14
From Real-World Regulations to Concrete Norms for Software Agents
its users are affected by real-world regulations. Consequently, SAs that act on behalf
of their human owners are subject to real-world regulations as well [12]. Neglecting
the question of how legal acts by SAs should be interpreted, nevertheless the problem
arises that SAs as actors in the Internet need to understand the legal context in which
they are acting. Hence when performing legal acts for their principals, SAs need to
understand the corresponding human regulations [18] in order to be able to assess
when and under which circumstances a regulation is violated and when not and what
punishment might follow. One possible relevant issue is the mere consideration of
rules and sanctions, especially when considering the communication platforms and
the relations between SAs and platforms: if SAs don't abide by the rules, probably
they may be put out of the platform and, eventually, they might even be totally
destroyed or "murdered" [7]. But another important issue, especially when
considering the will of the SA in legal relations, has to do with the consideration of
legal rules and the possibility that SAs actually know them and adopt certain
standards of behavior according to the legal rules. However, is it reasonable to expect
that SAs behave in accordance with legal rules? [13]
This will be especially relevant in situations of on-line dispute resolution, which
results in the moving of already traditional alternative dispute resolution "from a
physical to virtual place" [11]. This allows the parties not just the ease of litigation,
but mainly a simple and efficient way of dealing with disputes, saving both "temporal
and monetary costs" [26]. Several methods of Online Dispute Resolution (ODR) may
be considered, "from negotiation and mediation to modified arbitration or modified
jury proceedings" [21].
Anyway, regardless of the method to be adopted, we must confront ourselves with
the existence of different ODR systems, including legal knowledge based systems
appearing as tools that provide legal advice to the disputant parties and also "systems
that (help) settle disputes in an online environment" [17]. Yet, it is undoubtful that
Second Generation ODR in which ODR systems might act "as an autonomous agent"
[32] are also on the edge of becoming a way of solving disputes. In considering this
possibility, it is not our purpose to question the Katsch vision of the four parties in an
ODR process: the two opposing parties, the third party neutral and the technology
that works with the mediator or arbitrator [25]. But here, it must be assumed a gradual
tendency to foster the intervention of SAs, acting either as decision support systems
(DSS) [11] or as real electronic mediators [32]. Surely, this latest role for SAs would
imply the use of artificial intelligence techniques through case based reasoning (CBR)
and information and knowledge representation. "Models of the description of the fact
situations, of the factors relevant for their legal effects allow the agents to be supplied
with both the static knowledge of the facts and the dynamic sequence of events" [32].
Of course, representing facts and events would not be sufficient for a dispute
resolution, the SA in order to perform actions of utility for the resolution of the
dispute also needs to know not only the terms of the dispute but also the rights or
wrongs of the parties [32], and to foresee the legal consequences of the said facts and
events. Actually, we may well have to consider the issue of software agent really
understanding law or, in the way the Dutch doctrine has been discussing about legal
reasoning by software agents and its eventual legal responsibility: "are law abiding
agents realistic?" [13]
15
T. Balke et al.
The problem that arises when SAs are to operate under real world conditions is that
human regulations are usually written in a quite abstract way and are often open to
interpretation [22]. The main reason for this is to cover a large number of cases with
the same legal text and to keep regulations stable over a longer period. Thus if being
formulated in an abstract way, the same legal text can be applied to several scenarios
and only its interpretation needs to be adapted [39]. For instance, German regulations
on the obligation in kind, e.g. obligations of a seller who has not sold a specific item,
but an item of a certain kind are as follows: (§243 German Civil Code (BGB) [1]):
(1) A person who owes a thing defined only by class must supply a thing of average
kind and quality.
(2) If the obligor has done what is necessary on his part to supply such a thing, the
obligation is restricted to that thing.
In this case "average kind and quality" and "what is necessary" are abstract
terms/actions that (on purpose) are not properly defined, so that the number of
accepted ways for the debitor to fulfill his obligation(s) in kind can be extended
without changing existing laws. Furthermore, the study of law itself is not a natural
science but is based on hermeneutics where coherence and context are used to solve a
given problem. Thus, in the example the fulfillment is linked to the contextual
circumstances, leaving more room for interpretation on both sides.
As mentioned earlier, this abstraction and possibility of multiple interpretations that
is positive for humans pose severe problems when trying to implement them for SAs
where meaning should be precise and unambiguous. In order to tackle this problem,
this paper will present a cased-based reasoning (CBR) approach, in which a context
depended knowledge-base is set up that can be used for terminological interpretations
and comparisons by the SAs. In detail the paper is structured as follows: in order to
lay the foundations for the CBR approach, related work dealing with the question of
representing knowledge and regulations for SAs will be presented and compared to
CBR in chapter 2. Afterwards, in chapter 3.1 CBR and its six steps will be illustrated
in more detail. Last but not least, in chapter 3.2 the CBR model will be used to
analyze the example just mentioned in the last paragraph. The paper will close with a
short summary and conclusion.
2. Related work
After briefly explaining the problem of "translating" abstract human regulations for
SAs, in this chapter the related work will be presented. Therefore existing approaches
to represent information and rules shall be analyzed. As however, a multiplicity of
ways to represent information and regulations exists so far, this paper tries to classify
them into 4 categories - namely rule-based systems, ontologies, semantic webs and
case-based reasoning systems [20] - and will analyze the categories respectively.
16
From Real-World Regulations to Concrete Norms for Software Agents
2.1 Rule-Based Systems
As the name already indicates, rule-based systems are composed of a finite number of
rules. These rules normally can be formulated as conditional clauses of the following
form:
IF condition A holds, THEN it can be concluded that statement B is true as well. (If A
then B.)
Thereby the "if"-part of the rule is called proposition or left hand side whereas the
"then"-formulation is referred to as conclusion or right hand side. Besides these rules,
the knowledge base in rule-based systems consists of facts. Facts, in general, are
elements that can be described by a finite amount of discrete values [3]. The
coherences between the elements are represented by rules. Both components, the
rules and the elements, form the abstract knowledge of the rule-based system.
In order to apply the abstract knowledge to a new context, such as in the case of the
context-depended "obligations in kind" mentioned in chapter 1, a detailed context
description (i.e. concrete or case-specific knowledge) as well as an inference
mechanism are required. Depending on the application, the inference mechanism can
either be applied data-driven (forward-linked) or goal-oriented (backward-linked). In
the first case, the case specific knowledge is used as initial point for the reasoning
process. Starting from the fulfilled assumptions, the rules are used to infer about the
truth of the concluding rules. Subsequent, the deduced facts on their part are used as
initial points for the further inference process. In contrast, the goal-oriented approach
uses the opposite conclusion-direction. Thus, the final situation is taken as initial
point and all rules are checked by moving backwards, like in a decision tree where
starting from the top-node all subjacent edges and nodes are verified (see figure 1).
Figure 1. The tree structure of rule-based systems
When judging the applicability of rule-based systems for the "translation"-problem
mentioned in the introduction it has to be noticed, that although they foster a well
17
T. Balke et al.
structured analysis, they do not seem applicable. One reason for this is that in rule-
based systems all possible situations (or facts) and rules need to be known in advance,
leaving not only the problem of pre-definition, but this invokes such a large number
of propositions and rules that need to be defined (if one wants to map everything for
the SA) that the systems consistency and transparency are more than in danger.
2.2 Ontologies
Another method discussed in literature to move from abstract human regulations to
concrete ones for SAs are ontologies (see [39] for example), as their formulation and
usage enables programmers of SAs to separate the knowledge of a system (including
the terminological knowledge) and the processes. As a consequence of this separation
the knowledge can be analyzed, processed and expanded independent of the
processes and can be used by SAs for communication purposes. Thereby all
knowledge that needs to be used for the communication of SAs needs to be
completely represented by the ontology. An ontology itself is a description (like a
formal specification of a program) of the concepts and relationships that can exist for
an agent or a community of agents. Thus, in the ontology, the individual
communication elements correspond to language constructs that are arranged
according to a standardized, predetermined form. Besides this integrative form of
the communication elements the content of the messages is restricted as well [23].
Although this restriction seems delimiting, it nevertheless ensures that the
communication partners use a certain common vocabulary and understand the same
terms. This is comparable to the human language: a reasonable communication is
only possible if all persons participating associate the same meaning with the same
terms. For SAs the establishment of a common ontology means that abstract terms,
although having a number of meanings in human interpretations, can be translated to
a specific terms that are understood by all SAs the same way, solving the problem of
making abstract terms understandable for SAs. Although this idea sounds reasonable
and might be applicable for very specific scenarios, as the rule-based systems it
brings along complexity problems as soon as these specific scenarios are left. Thus,
although ontologies offer standardized text constructs that might be used for
negotiation, often these are not being used in the specifications and negotiations (e.g.
for reasons of the lack of adaptability of the ontological terms to new situations), but
free-text fields are used instead. This however, makes ontologies disadvantageous for
bridging the gap between abstract human regulations and specific ones for SAs and
illustrates the need for a better concept to solve the problem.
2.3 Semantic Nets
The last group of methods of solution that shall be discussed in this paper - besides
CBR approaches - are semantic nets, which were first invented for computers by
Richard H. Richens of the Cambridge Language Research Unit in 1956. A Semantic
net is net, which represents semantic relations between the concepts. This is often
used as a form of knowledge representation. It is a directed or undirected graph
18
From Real-World Regulations to Concrete Norms for Software Agents
consisting of vertices, which represent terms and concepts, and edges that represent
the relations between the terms [38] (see figure 2 for example).
Figure 2. Semantic Nets
By using semantic nets for concepts and terminologies, SAs are given the capability
to understand and process freely drafted texts by referring to the components of the
nets and their structure to one another. Although this solves one problem occurring
when applying ontologies, several further problems remain. Thus, although semantic
nets are appropriate for specifying fuzzy terms that consist of several elements (i.e.
items with vague component specifications), it is difficult to construct semantic nets
that help to define single terms that are hardly divisible such as the term "average"
when referring to the kind and quality when dealing with obligations in kind.
3. Cased-based reasoning
As a result of the limitations of the approaches presented so far, this paper will
present a mechanism that overcomes these limitations and helps to solve the
translation problem introduced in chapter 1: the CBR approach. The fundamental idea
19
T. Balke et al.
of this approach is not to try to "translate" abstract terms directly, but - as done in
hermeneutics - to use coherence and context to address the problem [8]. Thereby it is
assumed that similar cases normally tend to have similar solutions and similar terms
normally tend to have similar meanings, even if they emerge against different
backgrounds. Consequently the knowledge gained from solving earlier translation
problems can be used as a first approximation when new translation problems appear
[36]. This idea of cases that are used for drawing analogies is very well known in
legal practice [9] and therefore has the advantage of being [10] widely discussed and
reasoned about. A concrete case of case-based reasoning at least consists of a
description of the problem (i.e. the abstract terms) and the solution found therefore
(i.e. the translation in a specific context). In addition the solution to the problems can
be associated with a quality assessment or justifications why a specific solution was
chosen for a specific case. The individual cases are stored in a knowledge base which
can be resorted to when a new problem arises.
3.1 The 6 steps of Case-Based Reasoning
The six step CBR process model that will be used in this paper was first presented by
Roth-Berghofer and Iglezakis [34] who expanded the often cited CBR model of
Aamodt and Plaza [2]. The model consists of the six steps retrieve, reuse, revise,
retain, review and restore that are integrated into two separate phases, the application
and the maintenance phase (see figure 3).
Retrieve. Given a target problem, in the first phase of the model, similar cases 1 that
are relevant for solving the new problem are retrieved cases from memory. A case
consists of a problem, its solution, and, typically, annotations about how the solution
was derived. For example, suppose an agent wants to buy a specific complex grid
service (that uses CPU time, disk space and memory for its calculations) in the name
of his principal. So far, however he has never bought such a service before and is no
familiar with the vocabulary applied. Thus, being a novice in this area, the most
relevant experience he can recall is one in which he successfully bought some virtual
disk space, i.e. a resource that the service he wants to buy now consists of [19]. The
procedure he followed for buying the disk space, together with the justifications for
decisions made along the way, constitutes the agent's retrieved case.
Reuse. After the retrieval of similar cases, these solutions from the previous cases
have to be mapped to the target problem. This is done in the reuse-phase. The
mapping itself may involve adapting the solution as needed to fit the new situation. In
1 For more information about how to retrieve similar cases and to draw analogies between them
see [29] or [14] for example. They, for example, propose to use a memory that organizes
experiences (cases) based on generalized episodes. These structures hold generalized
knowledge describing a class of similar episodes. An individual experience is indexed by
features which differentiate it from the norms of the class (those features which can
differentiate it from other similar experiences). As a new experience is integrated into memory,
it collides with other experiences in the same generalized episode which shares its differences.
This triggers two processes. Expectations based on the first episode can be used in analysis of
the new one (analogy). Similarities between the two episodes can be compiled to form a new
memory schema with the structure just described (generalization) [28].
20
From Real-World Regulations to Concrete Norms for Software Agents
the grid service example, this would for example mean that the agent must adapt his
retrieved solution to focus on complex services instead of "simple" resources.
Revise. Having mapped the previous solution to the target situation, the next step is
to test the new solution in the real world (or a simulation) and, if necessary, revise it.
Suppose the agent adapted his grid resource solution by adding the costs for the
individual resources up in order to have an idea about the price for the service. After
this, he discovers that the aggregated costs for the individual resources are much
higher than the costs for the complex service and he offered the seller of the service
too much money for it, as his cost calculation did not account for this interrelation -
an undesired effect. This suggests the following revision: concentrate on market
prices when trying to calculate the costs for a service and do not aggregate the costs
of the individual resources instead.
By finishing the revision, the application phase (i.e. the actual problem solving)
itself can be closed 2 . However for a CBR system to function properly the knowledge
base that it is based on, needs to be sustained. This is done in the maintenance phase
which consists of the three sub-phases retain, review and restore.
Retain. After the solution has been successfully adapted to the target problem,
together with the resulting experience, it should be stored as a new case in the
memory i.e the knowledge base. The agent, accordingly, records his newfound
procedure for buying grid services, thereby enriching his set of stored experiences,
and better preparing him for future grid service transactions. A second purpose of the
retain step is to modify the similarity measures by modifying the indexing structures.
However, modifications like this should only be implemented in case-based reasoning
if it is possible to track the changes or better measure the impact of those changes.
2 At first glance, CBR (and especially its application phase) may seem similar to the rule-
induction algorithms of machine learning as it starts with a set of cases or training examples
and forms generalizations of these examples, albeit implicit ones, by identifying commonalities
between a retrieved case and the target problem. The key difference, however, between the
implicit generalization in CBR and the generalization in rule induction lies in the point when
the generalization is made. A rule-induction algorithm draws its generalizations from a set of
training examples before the target problem is even known; that is, it performs eager
generalization. In contrast, CBR starts with the target problem and delays implicit
generalization of its cases until testing time.
21
T. Balke et al.
Figure 3. The six steps in CBR
Review. The review step considers the current state of the knowledge containers and
assesses their quality. For this purpose appropriate measures need to be found. In
literature two fields of corresponding kinds of measures can be distinguished:
syntactical measures (i.e. measures that do not rely on domain knowledge) like
minimality, simplicity, uniqueness, etc. [33], and semantical measures (i.e. measures
using domain knowledge) which check whether the cases are (still) relevant for
example [37].
Restore. Finally, the last phase comes into play in case in the review phase it was
identified that the quality level of the cases is not as desired. In this case measures to
lift the quality level above the critical value are suggested and if approved are being
implemented [34].
After having had a look at the CBR model and its six steps in general, in the next
chapter, the model shall be applied to the obligation in kind example given in the
introduction in order to show the CBR potentials for helping to make abstract terms
understandable for SAs. Thereby special focus will be on the potential prerequisites
and problems within the six steps as well as potential solutions to these.
3.2 Applying the Case-Based Reasoning Approach
After explaining the general CBR approach, the question arises how it can help with
"translation" abstract legal terms for SAs. Therefore the example given in the
introduction (concerning the "obligations in kind") shall be recalled. One example
22
From Real-World Regulations to Concrete Norms for Software Agents
where this regulation applies is the domain of cloud computing. The term cloud
computing describes the idea that similar to other services - such as electrical power,
the telephone, gas or water, in which the service providers seek to meet fluctuating
customer needs, and charge for the resources based on usage rather than on a flat-rate
basis - IT-services are sold over the Internet [15]. Examples of such IT-services are
storage space, server capacity, bandwidth or computer processing time. Cloud
computing envisions that in contrast to traditional models of web hosting where the
web site owner purchases or leases a single server or space on a shared server and is
charged a fixed fee, the fixed costs are substituted by variable costs and he is charged
upon how much he actually uses over a given period of time. The negotiation of the
cloud services is performed by SAs that automatically react to changes in the resource
needs and buy the additional resources needed. The contracts thereby do not
concentrate on specific resources (e.g. a specific part of a certain server as storage
space or a specific processor that shall be used for the calculations) but feature
obligations in kind (i.e. only the general "storage" service, etc. is fixed in the
contracts). The reason for this is that the service suppliers try to optimally use their
capacity and therefore allocated and reallocate all services continuously depending on
the total demand in the network. That's why in cloud computing contract normally
service-packages are offered, leading to problems in the comparability for software
agents. This problem is intensified by the fast development in the IT sector, leading to
a steady increase in the possible component that can be used for a cloud service.
So how could CBR help to solve this translation problem, i.e. how can SAs learn to
reason about very general legal terms such as "average kind and quality" and "what is
necessary", etc.? To start the explanation, we would like to recall the general CBR-
idea: namely the usage of coherence and context to address. As mentioned in chapter
3.1 it thereby is assumed that similar cases normally tend to have similar solutions
and similar terms normally tend to have similar meanings, even if they emerge
against different backgrounds. This means that in order to be applicable for the
"translation"-example, the SA needs a knowledge base that is filled with at least a few
cases. If no similar cases exist, the SA first of all needs to be trained, meaning that it
has to pass the decision to his principal who then makes that decision and gives the
result to the SA who then is able to fill his knowledge container. As the cases are the
fundamental elements of CBR and everything else is based upon them, the case-
definition is a first very important step to look at. For practical reasons, normally all
cases have a particular name, a set of empirical circumstances or facts, and an
outcome representing the results of the problem for the decision, solution or
classification it poses [16]. These characteristics of a case are then written down in a
systematical structured way, such as in form of tables or vectors, etc. Looking at the
cloud example, the set of facts might include the original contract formulations
(including the related juristic paragraphs and their formulations), the services
requested delivered and some quality criteria of the services (e.g. availability or
speed), whereas the outcome description could comprehend in how far the measured
quality criteria represent the expected ones and whether any difference can be
attribute to the obligation in kind. Once, a knowledge based with a few cases exists,
the reasoning process can be started, i.e. the SA has to find a similar case and needs
to go on by analyzing which decisions were made in this case and why. A very
general scheme for the deduction step was presented by Ashley [9]:
23
T. Balke et al.
Start: Problem description.
A: Process problem description to match terms in case
database index.
B: Retrieve from case database all candidate cases
associated with matched index terms.
C: Select most similar candidate cases not yet tried.
If there are no acceptable candidate cases, try
alternative solution method, if any, and go to F.
Otherwise:
D: Apply selected best candidate cases to analyze/solve
the problem. If necessary, adapt cases for solution.
E: Determine if case-based solution or outcome for
problem is successful.
If not, return to C to try next candidate cases.
Otherwise:
F: Determine if solution to problem is success or
failure, generalize from the problem, update index
accordingly and Stop.
Based on this general algorithm, in literature five paradigmatic approaches
comparing the existing knowledge base with new cases can be found; these are:
statistically-oriented, model-based, planning / design-oriented, exemplar-based, and
adversarial or precedent-based approaches 3 .
Out of these five, for the cloud example, the model-based paradigm is of special
interest, as this paradigm, cases are examples explained in terms of a theoretical
model of the domain task. Thus, if the SA is confronted with a new case, it has to
determine, if the past explanations (e.g. of the legal terms) apply [30]. Similar cases
in the cloud computing-"translation" example might for example be transactions
about IT services that included §243 of the German Civil Code which the SA has
concluded before. Starting from these similar cases, in the next step, the SA is to
analyze the similarities between his new problem and the old cases. Thereby he has to
include the context of the cases in its reasoning. Finally, if a decision is made
concerning the interpretation or the translation of the new terms, the mapping needs
to be tested in reality. This can either be done by the software agent sending its
decision to its principal for validation purposes or by closing the deal and waiting for
the outcome (which is then checked against the expected outcome). Finally, after the
"translation"-problem is being solved and the outcome is clear in a next step, the
quality of the new solution needs to be assessed. This is either done by comparing the
achieved result with the expected one or by transferring the evaluation to the principal
who can make more elaborated decisions. Afterwards the SA can decide whether to
include this new case in the knowledge base or not. Normally it will choose to do so
if the new case expands its knowledge base in a sensible way, e.g. if it has not stored
any cases concerning the vocabulary of §243 of the German Civil Code before. This
knowledge adaptation is completed by maintaining the knowledge base. Thus in the
legal context it might happen that a paragraph or a law is changed or interpreted
differently in the course of time.
3 For a detailed description of the paradigms see [9].
24
From Real-World Regulations to Concrete Norms for Software Agents
4. Conclusions
As mentioned in the introduction, when wanting to move to electronic environments
where intelligent software agents not only conclude contracts on behalf of their
human owners but also may participate in dispute resolution, many challenges need to
be overcome. One of them is the problem of the abstractness of human regulations.
The paper presented several approaches that can be found in literature (e.g.
ontologies, etc.) trying to tackle the problem, which however have several drawbacks
and consequently may not be the best choice. That is why the paper presented the
CBR reasoning concept and explained how it could help to solve the problem. In
contrast to many other approaches, CBR has the advantage of being applicable even
to the new problems to be solved (e.g. the understanding of new abstract terms) 4 if
the problem is badly structured or described incompletely, if the knowledge base
starts with a relatively small number of cases or if the rules between the different
components are not all known [27, 31]. For this reason and due to its relative
simplicity, in the view of the authors, it is well suited for addressing the "translation"-
challenges lying ahead and should be researched in more detail.
5. Acknowledgements
The work of Paulo Novais and Francisco Andrade described in this paper is included
in TIARAC - Telematics and Artificial Intelligence in Alternative Conflict Resolution
Project (PTDC/JUR/71354/2006), which is a research project supported by FCT
(Science & Technology Foundation), Portugal.
5. References
1. German civil code (bgb). DTV-Beck, September 2008. 62nd edition.
2. Aamodt and E. Plaza. Case-based reasoning: Foundational issues, methodological
variations, and system approaches. AI Communications, 7(1):39-59, 1994. IOS Press.
3. Abraham. Rule-based expert systems. In P. H. Sydenham and R. Thorn, editors,
Handbook of Measuring System Design, pages 909-919. John Wiley & Sons, 2005.
4. F. Andrade, P. Novais, J. Machado, and J. Neves. Contracting agents: legal personality
and representation. Intelligence and Law, 15(4):357-373, 2007. ISSN 0924-8463.
5. F. Andrade, P. Novais, J. Machado, and J. Neves. Intelligent contracting: Software agents,
corporate bodies and virtual organizations. In Establishing The Foundation of
Collaborative Networks, volume 243, pages 217-224. Springer Boston, 2007.
6. F. Andrade, P. Novais, and J. Neves. Divergence between will and declaration in
intelligent agent contracting. In ICAIL 2007 - Eleventh International Conference on
Artificial Intelligence and Law, Stanford University, Stanford, California, USA, June 4-8
2007, pages 289-290. ACM Press, 2007. ISBN 978-1-59593-680-6.
4 Although CBR reasoning can be applied if only a small knowledge base is available, the more
cases it can build on the better it tends to work.
25
T. Balke et al.
7. M. Apistola, F. M. T. Brazier, O. Kubbe, A. Oskamp, J. E. J. Prins, M. H. M. Schellekens,
and M. B. Voulon. Migrating agents: Do sysadmins have a license to kill? In Proceedings
of the 3rd International SANE Conference (SANE 2002), 2002.
8. K. D. Ashley. Arguing by analogy in law: A case-based model. In D. H. Helman, editor,
Analogical Reasoning, pages 205-224. Kluwer Publishers, 1988.
9. K. D. Ashley. Case-based reasoning and its implications for legal expert systems.
Artificial Intelligence and Law, 1(2-3):113-208, 1992.
10. K. D. Ashley. An ai model of case-based legal argument from a jurisprudential viewpoint.
Artificial Intelligence and Law, 10:163-218, 2002.
11. E. Bellucci, A. Lodder, and J. Zeleznikow. Integrating artificial intelligence,
argumentation and game theory to develop an online dispute resolution environment. In
ICTAI-2004 - 16th IEEE International Conference on Tools with Artificial Intelligence,
pages 749-754, 2004.
12. G. Boella, L. van der Torre, and H. Verhagen. Introduction to the special issue on
normative multiagent systems. Autonomous Agents and Multi-Agent Systems, 17:1-10,
2008.
13. F. M. T. Brazier, O. Kubbe, A. Oskamp, and N. J. E. Wijngaards. Are law-abiding agents
realistic? In Proceedings of the workshop on the Law of Electronic Agents (LEA2002),
pages 151-155, 2002.
14. M. H. Burstein. A model of learning by analogical reasoning and debugging. In
Proceedings of the National Conference on Artificial Intelligence, Washington, D. C.,
1983.
15. N. G. Carr. It doesn't matter. Harvard Business Review, pages 41-49, May 2003.
16. Chorley and T. Bench-Capon. Agatha: Using heuristic search to automate the construction
of case law theories. Artificial Intelligence and Law, 13:9-51, 2006.
17. De Vries, R. Leenes, and J. Zeleznikow. Fundamentals of providing negotiation support
online: the need for developping batnas. In Proceedings of the Second International ODR
Workshop, pages 59-67, Tilburg, 2005. Wolf Legal Publishers.
18. F. Dignum. Agents, markets, institutions, and protocols. The European AgentLink
Perspective, pages 98-114, 2001.
19. T. Eymann, M. Reinicke, W. Streitberger, O. Rana, L. Joita, D. Neumann, B. Schnizler,
D. Veit, O. Ardaiz, P. Chacin, I. Chao, F. Freitag, L. Navarro, M. Catalano, M. Gallegati,
G. Giulioni, R. C. Schiaffino, and F. Zini. Catallaxy-based grid markets. Multiagent and
Grid Systems, 1(4):297-307, 2005. IOS Press.
20. O. Geibig. Agentenbasierte Unterstützung Öffentlicher Ausschreibungen von
Bauleistungen unter Verwendung von Methoden der Künstlichen Intelligenz. PhD thesis,
Universität Duisburg-Essen, 2008.
21. J. Goodman. The pros and cons of online dispute resolution: an assessment of cyber-
mediation websites. Duke Law and Technology Review, 4, 2003.
22. D. Grossi and F. Dignum. From abstract to concrete norms in agent institutions. In
Lecture Notes in Computer Science, volume 3228. Springer, 2005.
23. T. R. Gruber. Toward principles for the design of ontologies used for knowledge sharing.
International Journal of Human-Computer Studies, 43(5-6):907-928, November 1995.
Academic Press, Inc., Duluth, MN, USA.
24. R. H. Guttman, A. G. Moukas, and P. Maes. Agent-mediated electronic commerce: a
survey. The Knowledge Engineering Review, 13(2):147-159, 1998.
25. E. Katsch and J. Rifkin. Online dispute resolution - resolving conflicts in cyberspace.
Jossey-Bass Wiley Company, 2001.
26. L. Klaming, J. Van Veenen, and R. Leenes. I want the opposite of what you want:
summary of a study on the reduction of fixed-pie perceptions in online negotiations. -
"expanding the horizons of odr". In Proceedings of the 5th International Workshop on
Online Dispute Resolution (ODR Workshop'08), pages 84-94, 2008.
26
From Real-World Regulations to Concrete Norms for Software Agents
27. J. Kolodner. Case-Based Reasoning. Morgan Kaufmann Publishers, San Mateo, 1993.
28. J. Kolodner, R. Simpson, and K. Sycara. A process model of case-based reasoning in
problem solving. In Proceedings of the Ninth International Joint Conference on Artificial
Intelligence, pages 284-290, 1985.
29. J. L. Kolodner and R. L. Simpson. Experience and problem solving: a framework. In
Proceedings of the Sixth Annual Conference of the Cognitive Science Society, pages 2-9,
Boulder, CO., 1984.
30. P. Koton. Using Experience in Learning and Problem Solving. PhD thesis, MIT, 1988.
31. D. Leake. Cbr in context: The present and future. In D. Leake, editor, Case-Based
Reasoning: Experiences, Lessons, and Future Directions, pages 1-30. AAAI Press / MIT
Press, 1996.
32. G. Peruginelli and G. Chiti. Artificial intelligence in alternative dispute resolution. In
Proceedings of the workshop on the Law of Electronic Agents (LEA 2002), 2002.
33. T. Reinartz, I. Iglezakis, and T. Roth-Berghofer. Review and restore for case based
maintenance. In E. Blanzieri and L. Portinale, editors, Advances in Case-Based Resoning,
pages 247-259. Springer, 2000.
34. T. Roth-Berghofer and I. Iglezakis. Six steps in case-based reasoning: Towards a
maintenance methodology for case-based reasoning systems. In H.-P. Schnurr, S. Staab,
R. Studer, G. Stumme, and Y. Sure, editors, Professionelles Wissensmanagement:
Erfahrungen und Visionen (Proceedings of the 9th German Workshop on Case-Based
Reasoning (GWCBR)), pages 198-208. Shaker-Verlag, 2001.
35. S. Russell and P. Norvig. Artificial Intelligence - A Modern Approach. Prentice Hall,
2002.
36. R. Schank. Dynamic Memory: A Theory of Learning in Computers and People.
Cambridge University Press, 1982.
37. Smith and M. Keane. Remembering to forget: A competence.preserving case deletion
policy for case-based reasoning systems. In Proceedings of the 13th International Joint
Conference on Artificial Intelligence, pages 377-382, 1995.
38. J. F. Sowa. Semantic networks. In S. C. Shapiro, editor, Encyclopedia of Artificial
Intelligence. Wiley, 1987.
39. J. Vázquez-Salceda, H. Aldewereld, D. Grossi, and F. Dignum. From human regulations
to regulated software agents' behavior. Artificial Intelligence and Law, 16(1):73-87, 2008.
Kluwer Academic Publishers.
27