=Paper=
{{Paper
|id=Vol-1283/paper42
|storemode=property
|title=
A Roadmap for Self-Evolving Communities
|pdfUrl=https://ceur-ws.org/Vol-1283/paper_42.pdf
|volume=Vol-1283
|dblpUrl=https://dblp.org/rec/conf/ecsi/OsmanS14
}}
==
A Roadmap for Self-Evolving Communities==
https://ceur-ws.org/Vol-1283/paper_42.pdf
A Roadmap for Self-Evolving Communities
Nardine Osman and Carles Sierra
Artificial Intelligence Research Institute (IIIA-CSIC), Bellaterra, Barcelona, Spain
Abstract. Self-organisation and self-evolution is evident in physics, chem-
istry, biology, and human societies. Despite the existing literature on the
topic, we believe self-organisation and self-evolution is still missing from
the IT tools (whether online or offline) we are building and using. In
the last decade, human interactions have been moving more and more
towards social media. The time we spend interacting with others in vir-
tual communities and networks is tremendous. Yet, the tools supporting
those interactions remain rigid. This position paper argues the need for
self-evolving software-enabled communities, and proposes a roadmap for
achieving this required self-evolution. The proposal is based on building
normative-based communities, where community interactions are regu-
lated by norms and community members are free to discuss and modify
their community’s norms. The evolution of communities is then dictated
by the evolution of its norms.
Keywords: norms, interaction protocols, self-evolution
1 Motivation
The concept of social self-organisation has deep roots in political theory. In
the Leviathan, Hobbes expresses the idea that the social structure, the State
or res publica, is the result of a social contract or pact. The social contract is
an agreement among free individuals in order to surrender power to a central
authority in order to get a guarantee of peace. In essence, a social contract is
the implicit acceptance of self-limiting individual freedom in order to guarantee
self-preservation and a better life. The concept of social contract implies that
individuals reflect on the community governance in order to decide on the means
to reach commonwealth.
This capacity of individuals to reflect and decide on community governance
is missing in the IT tools we are currently building. How individuals interact
is decided by groups of engineers that design tools, determine the individuals’
privacy levels, hinder some from performing certain tasks, and all this without
the individuals having a say. We argue that it is time for us to take control of
the evolution of our own virtual communities, which we refer to as software-
enabled communities — examples of such communities are those based on social
networks or any other community whose management relies on software, such as
customer relationship management systems (CRMs) or conference management
systems. To address this issue, we propose normative-based communities, where
2
community interactions are regulated by norms and community members are
free to discuss and modify their community’s norms (or social contracts), while
respecting their free will. The roadmap for self-evolution is then dictaded by
the lifecycle of norms, and the research required for supporting this lifecycle is
varied and multidisciplinary — from social sciences and legal studies, to learning
mechanisms, agreement technologies, natural language processing, formal logics,
norm enforcement and regimentation, human computer interaction, and other
software engineering techniques.
It is true that the notion of self-organisation is not new. Self-organisation,
in general, refers to how a system reorganises to adapt to changes to its goals
or environment, where the reorganisation is driven from within the system as
opposed to some external control. In many cases, self-organisation is associated
with emergence, where a structure appears at a higher level without it being
represented at a lower level. Self-organisation has been studied in a number
of areas, such as biology, chemistry, geology, sociology, as well as information
technology [26]. In AI, self-organisation has mostly been inspired by naturally
existing self-organisation models, although some new mechanisms have been de-
signed specifically for software applications [27]. For example, self-organisation
may be found in bio-inspired robot teams, where insect-based technology is used
to aid robots self-organise to accomplish a task [44]. Another example is intru-
sion detection in network security, which is inspired from the natural immune
system [20]. Swarm intelligence may be used to mimic insect foraging behaviour
on the coordination and control of data network traffic [48]. With the notions of
self-adaptability and long-lasting evolvability in mind, it has also been proposed
that natural ecosystems can inspire building ecosystems of services [47].
In this paper, however, we do not propose to follow traditional self-organisation
AI techniques, but to open a new direction in AI research with a novel method for
self-evolving software-enables communities. The proposed research is original in
the sense that we do not simply talk about self-organising software that usually
imitates existing natural systems, but we study how the human users’ evolving
needs consciously direct evolution. We essentially propose to provide the human
users with learning mechanisms that help them learn the best evolution path,
along with agreement technologies that help them discuss, argue, and agree on
their preferred path of evolution. The system then takes care of the formalisa-
tion, operationalisation, and enforcement of the agreed upon norms. This is a
fundamentally different approach from existing self-organising systems.
2 Roadmap for Self-Evolving Communities
In this paper, we argue that it is time for us to take control of the evolution
of our own virtual communities. To address this issue, we propose normative-
based communities, where community interactions are regulated by norms and
community members are free to discuss and modify their community’s norms.
Norms are the rules that govern behaviour in groups and societies [8]. They
motivate and influence individual actions by dictating what values, beliefs, atti-
3
tudes, and behaviours are deemed appropriate or not. Social norms have been ex-
tensively studied by anthropologists, sociologists, philosophers, and economists
in the hope of understanding how they motivate individual actions, influence
market behaviour, and so on. In multiagent systems, the study of norms gained
tremendous attention due to the critical issue of coordinating agent behaviour
and actions. Although, unlike other social sciences, the distinction between social
and legal norms has not been concrete in the field of computer science.
We adopt the idea of using norms to control, or mediate, community be-
haviour. This is motivated by the fact that software is usually engineered based
on some notion of norms in mind; furthermore, the use of norms allows human
users to discuss their interactions without the need for any technical knowledge
about the software actually mediating their interactions. We note that by speci-
fying a community through its norms, the evolution of the community becomes
dictated by the evolution of its norms. As such, the evolution of software-enabled
communities may be depicted by the lifecycle of norms, as illustrated by Figure 1.
"normsFormalisation
Automated
of Norms:
are translated from natural
language to a formal specification
Formalised
Norms
Norms
! community
Birth of Norms:
members # Operationalisation
Automated
of Norms:
collectively decide software is modified
on their norms to incorporate the norms
Community
Software
Interactions
$ Enforcement
Automated
of Norms:
software ensures community
interactions follow the norms
Fig. 1. The lifecycle of norms lays out the roadmap for self-evolving communities
We divide the lifecycle of norms in a software-enabled community into four
different stages. In the first stage, community members discuss and agree on their
community’s norms. If community members do agree on a new set of norms, then
the norms’ lifecycle enters its second stage, where the agreed upon norms are
automatically translated into some formal specification. Given the formal spec-
ification of norms, the lifecycle then enters the third stage, where the software-
enabled community interactions are modified to operationalise the new norms.
Finally, as the software runs to mediate community interactions, norms are en-
forced by ensuring that community interactions follow the agreed upon norms.
The details of these four stages are presented next.
4
2.1 Birth of Norms
In this stage, community members propose norm modifications (including the
abandoning and adoption of norms), discuss the different proposals amongst
themselves, and collectively agree on their community’s new (or modified) set of
norms.
An interdisciplinary research is highly recommended at this stage. For exam-
ple, social sciences can help understand the emergence of norms, their social
function, their impact on behviour, as well as understand the possible deviance
from norms. Legal studies can help understand the legal aspects of norms,
such as the enforcement of norms, the effectivity and validity of norms, and
the resolve of conflicting norms. Learning mechanisms can help learn from
a community’s past experience, or a similar community’s past experience, and
suggest norm modifications accordingly. Finally, to aid the collective agreement
on community norms, agreement technologies will be needed.
Concerning learning mechanisms, learning from data has been one of the main
subfields of artificial intelligence [2], yet the literature lacks the in-depth study of
learning the best norms for multiagent interactions. In multiagent systems, some
work has been carried out on norm synthesis [40]. Most norm synthesis (online)
approaches, such as [38, 46, 37, 23], are based on norm emergence techniques that
require agents to collaborate to synthesise their own norms. [29] does not require
the active participation of agents in norm synthesis as norms are synthesised
from the observation of agents’ interactions. The norm synthesis of [30] is based
on the capability of generalising norms, which allows characterising necessary
conditions for coordination, avoiding over-regulation.
Concerning agreement technologies, we note that this field is one of the vi-
brant and active fields in multiagent systems. Agreement technologies aim at
helping individuals collaboratively reach a decision, which is crucial when indi-
viduals are intelligent and autonomous, having their own goals and agendas to
fulfil. The field is based on a number of models and mechanisms, such as argu-
mentation and negotiation mechanisms, social choice theory and voting mecha-
nisms, and trust and reputation models.
Argumentation theory uses logical reasoning to illustrate how conclusions
may be drawn in cases of uncertainty and conflict. Argumentation frameworks
in multiagent systems [32] have mostly been built on Dung’s theory of abstract
argumentation [17], where a directed graph is used to represent arguments (the
nodes of the graph) and the attack relations between them (the directed links
of the graph). A ‘calculus of opposition’ is then applied to help determine the
winning argument. Dung’s framework has been extended in a number of direc-
tions, such as adding preferences [3], allowing attacks on attacks [7], and adding
weights to attacks [18]. One interesting extension to Dungs framework is one
that incorporates social voting and is used in social networks [25, 11].
Social choice theory is concerned with the representation and aggregation of
individual preferences. Voting mechanisms and ranking systems are considered
to fall under the social choice category. Social choice theory studies a variety of
aspects, such as the manipulation of a voting rule [10], the use of combinatorial
5
domains where voters may specify a list of preferences [9], and the efficiency and
fairness of voting rules [19].
A common issue that arises in building open distributed systems is the issue
of trust and reputation. Trust has become an increasingly important concept in
computer science [5, 22]. For example, there have been studies on the develop-
ment of trust in e-commerce [34], on mechanisms to determine which sources to
trust when faced with multiple conflicting information sources [54], and mech-
anisms for identifying which individuals to trust based on their past activity
[1]. Trust is an especially important issue from the perspective of autonomous
agents and multiagent systems [42]. In multiagent interactions, agents will have
to reason about how much should they trust other entities (in various contexts).
For reviews on trust in multiagent systems, we refer the reader to [33, 36].
Roadmap: In the context of self-evolving communities, research in the in-
formation and computer Science field should focus on two main issues. (1) We
should aim at building a system that is capable of automatically suggesting norms
that can help improve the quality of interactions in a community, based on learn-
ing from similar past experiences. Improving the quality of interactions may refer
to a number of issues, such as improving the individual user satisfaction, or im-
proving the efficiency of the interactions by helping community members achieve
their goals faster and easier. We note that learning mechanisms may also be
combined with other techniques, such as analogical reasoning [45] or coherence
theory [43]. (2) We should investigate the combination of different agreement
technologies to help community members agree on how should their community
evolve. For example, it would be interesting to study the combination of trust
and argumentation mechanisms, for instance, by modifying the reasoning pro-
cess to consider reputation/trust based measures of the strengths of arguments.
It would also be interesting to study voting algorithms mediated by trust, with
votes potentially weighted by the trustworthiness or degree of involvement in the
community. The outcome of this research work would be to aid community
members in agreeing on their community norms.
2.2 Automated Formalisation of Norms
Norms agreed upon by community members need to be formalised in a language
that can be understood by the system mediating community interactions. We
expect community members to be non-technical people who discuss and specify
norms in natural language. An automatic translator is then needed to translate
the norms from their natural language specification to some formal specification
that is comprehensible by the system.
The research that should support this stage should focus on natural lan-
guage processing, which will help perform the automatic translation, and for-
mal logics, which will define the formal language used to specify norms.
Concerning natural language processing, machine translation [49] is a sub-
field that translates one natural language into another. This sub-field has been
well studied and it has been even used in commercial applications, such as Google
Translate or Yahoo’s Babel Fish. The translation from a natural language to
6
some logic, on the other hand, has attracted much less attention in the natural
language processing field. Nevertheless, important research has been carried in
that direction. [50, 52] illustrates and discusses the use of existing state-of-the-art
in the automatic translation of regulatory rules in natural language into a ma-
chine readable formal representation. [39] translates a complete set of pediatric
guideline recommendations into a controlled language (Attempto Controlled En-
glish – ACE). [51] adopts and applies a controlled natural language to constrain
the domain of discourse in a sample discussion from an on-line discussion forums
for e-government policy-making. The controlled natural language helps eliminate
ambiguity and unclarity, and allows a logical representation of statements. Each
of the policy statements is then automatically translated into first-order logic.
[53] presents a linguistically-oriented, rule-based approach, for extracting condi-
tional and deontic rules from regulations specified in natural language. Finally,
[4, 6] present approaches for the logical representation of regulations.
Concerning the formal specification of norms, existing approaches may be
divided into two main categories: declarative approaches and procedural ones. We
note that in this paper, we refer to the declarative approaches as norms and the
procedural ones as interaction models. Declarative formalisms are concerned with
the expressiveness of norms, the formal semantics, and how to resolve conflicts
arising from an inconsistent set of norms. Declarative approaches are usually
based on deontic logic, which is the logic of duties. They deal with concepts like
permissions, prohibitions, and obligations, which help specify who can do what,
under what conditions, and so on.
Deontic-based policy languages have been used widely in hardware systems
and networks for security reasons, trust negotiation, access control, authorisa-
tion, and so on. [41] defines policies to be “one aspect of information which influ-
ences the behaviour of objects within the system”. [13] categorises policies into
two types. The first type covers obligation policies for managing actions, which
are usually event triggered condition-action rules. The basic concept is that spe-
cific events trigger specific actions, and the actions may only be executed if a
predefined set of conditions is satisfied. The second type covers authorisation
policies, which are usually used for access control.
In multiagent systems, several deontic based formal logics have been proposed
for defining a normative specification of agents interaction, such as [31] and [14].
In [31], it is proposed that a community should be defined whenever a group
of agents have some common goals and they need to act as a whole within the
society in order to fulfil those goals. They propose to define the roles within the
community, the relationships among them, which actions each role can do, and
how the obligations are distributed among the roles. Each role has associated
deontic notions that describe the role obligations, permissions and prohibitions.
[14] extends the BDI model of agents to include goals, obligations, and norms;
the proposal is based on providing a formal definitions of norms by means of
some variation of deontic logic that includes conditional and temporal aspects.
Roadmap: In the context of self-evolving communities, research should focus
on two main issues. (1) We should aim at building an automatic translator that
7
can translate norms specified in a natural language into some formal logic. (2)
We should select, or design, a logic that fulfills the following three requirements:
i) it should be expressive enough to capture the community members’ needs; ii)
it should be compatible with the natural language processing technique that needs
to translate norms specified in natural language into the logic; and iii) it should
be compatible with the software system that needs to operationalise those norms.
The outcome of this research work would be to have the agreed upon norms
translated into some formal logic that is ready to be operationalised by the system.
2.3 Automated Operationalisation of Norms
Given a formal specification of norms, the ultimate goal is to enforce those norms.
One approach to achieve this is through what is known as norm regimenta-
tion. In other words, the system mediating community interactions needs to be
modified in order to operationalise those norms.
The literature provides a variety of solutions that deal with specifying and
regulating interactions in multiagent systems based on the concept of following
social norms [40], such as having contracts and commitments [15], organisational
approaches [24], electronic institutions [16], distributed dialogues [35], and so on.
Two specifically interesting approaches are electronic institutions [16] and
the lightweight coordination calculus [35]. In [16], it is argued that open multi-
agent systems can be effectively designed and implemented as agent mediated
electronic institutions where heterogeneous (human and software) agents can
participate, playing different roles and interacting by means of speech acts. An
institution is defined by a set of roles that agents participating in the institu-
tion will play, a common language to allow heterogenous agents to exchange
knowledge, the valid interactions that agents may have structured in conversa-
tions, and the consequences of agents’ actions within an institution, captured by
obligations that agents acquire and fulfil.
The lightweight coordination calculus (LCC) [35] is a process calculus, based
on logic programming, that provides means of achieving coordination in dis-
tributed systems by enforcing social norms. The process calculus specifies what
actions agents can perform, when they can perform such actions, under what
conditions these actions may be carried out, and so on. However, unlike elec-
tronic institutions, there are no ‘governors’ that ensure that agents abide by
norms. Of course, like all the approaches above, these rules are associated with
roles rather than physical agents; and agents can play more than one role in more
than one interaction. This provides an abstraction for the interaction model from
the individual agents that might engage in such an interaction.
In the context of self-evolution, it is important to build a system whose
interaction models can operationalise norms requested by its uers. We note,
however, that not all norms are capable of being operationalised. For example, in
an e-commerece context, the system can operationalise the norm that states that
a buyer cannot rate the seller more than once, as the system can actually prevent
the buyer from doing so. However, the system cannot operationalise a norm
that states that only people with sufficient credit can bid, as credit is private
8
information that cannot be accessed and assessed by the system. To deal with
norms that cannot be operationalised, alternative norm enforcement methods
will need to be applied, such as applying sanctions (punishments and rewards).12
In this case, the system will need to ensure the enforcement of un-operationalised
norms by checking the members’ abidance to those norms and enforcing the
appropriate sanctions accordingly. To achieve this, it is important to design a
lightweight computational norm enforcement model that allows for the detection
of norm violations and the application of remedial actions. This work may build
upon existing work, as the literature is rich with norm engines that may be used
for resolving conflicting norms and applying appropriate sanctions [21, 28, 12].
Roadmap: In summary, in the context of self-evolution, research should
focus on two main issues: (1) building a mechanism that allows the system
to operationalise the norms agreed upon by the community members; and (2)
building a norm enforcement model that can check members’ abidance to un-
operationalised norms and enforce appropriate sanctions accordingly. The out-
come of this research work would be to have a system that is capable of enforcing
the norms agreed upon by the community members.
2.4 Automated Enforcement of Norms
Finally, running the software that mediates community interactions will allow
members of the community to interact, while ensuring the enforcement of the
norms. The research that will support this stage will need to focus on human
computer interaction techniques that are required to ensure an intuitive,
user friendly interaction for non-technical community members. Research will
also need to focus on software engineering techniques to ensure efficient
community interactions. However, the context of self-evolution imposes an addi-
tional and critical challenge: how can the system be modified at runtime when
different members may still be interacting together? Indeed, it is very unrealis-
tic to assume that all members will need to complete their current interactions
successfully and pause any future interactions for the system to get modified
(as future interactions will need to follow the new norms). As such, research
will need to address the issue of seamlessly adapting the system at runtime, and
it may take inspiration from existing software compatibility models that allow
different versions of an application to continue to communicate and collaborate.
Roadmap: In summary, in the context of self-evolution, research should
focus on two main issues: (1) building a user friendly interface that would allow
1
The literature usually refers to the operationalisation of norms as ‘norm regimenta-
tion’, and alternative norm enforcement methods (such as the application of sanc-
tions) as ‘norm enforcement’. In this paper, however, we overload the word ‘enforce’
as we use it to describe both approaches.
2
It may be argued that punishment and reward is not always the right approach for
motivating the abidance to norms. Furthermore, what may be considered a punish-
ment for one may be viewed as a reward for another. In this paper, although we
talk about punishments and rewards, we concur that sanctions may be labeled more
generally as the post-conditions of abiding with or breaking a norm.
9
community member to interact, following the norms, without any knowledge of
their interactions and their complexity, and in cases when users need to discuss
norms, norms and norm violations will need to be visualised in a user friendly
and comprehensible manner; and (2) engineering the system in such a way that
ensures efficient interactions and permits evolution at runtime in a seamless
manner that does not interrupt community interactions. The outcome of this
research work would be to have the system run and manage evolving normative-
based community interactions in an efficient, userfriendly, and seamless manner.
3 Conclusion
We argue that just like human communities, e-communities (or virtual communi-
ties) also need to self-evolve. Instead of creating numerous rigid systems, which is
very common in online social systems, what we should aim at instead is providing
tools for creating self-evolving systems that adapt to the community’s needs. We
believe different communities should be governed by different rules. These rules
should be an ever evolving set resulting from the aspirations of its members, and
not a rigid set defined by a corporation (or a system designer) that does not take
the community members’ interests at heart. Furthermore, for the community’s
rules to be effective, they need to be tailored to the specific community, such as
considering the character traits of the community’s members.
We believe that the notion of self-evolving communities will revolutionise the
way how software is build, as well as how we interact with software. We say
that in the context of social interactions, software does not need to be rigid, nor
customised for the individual, but adaptable to the group. Software engineers will
need to design intelligent software that is capable of evolving according to the
needs of the community as a whole, and users (or community members) should
become more active in shaping their community by discussing and agreeing on
their community rules.
In summary, the proposal is based on the traditional notion of mediating so-
cial interactions via norms. The roadmap for self-evolution is then dictaded by
the lifecycle of norms, which we divide into four different stages (see Figure 1):
(1) the birth of norms, (2) the automated formalisation of norms, (3) the auto-
mated operationalisation of norms, and (4) the automated enforcement of norms.
Varied, and even interdisciplinary, research will be required for supporting these
four stages, from social sciences and legal studies, to learning mechanisms, agree-
ment technologies, natural language processing, formal logics, norm enforcement
and regimentation, human computer interaction, and other software engineering
techniques.
References
1. Adler, B.T., de Alfaro, L.: A content-driven reputation system for the Wikipedia.
In: Proceedings of the 16th International World Wide Web Conference. Banff,
Alberta, Canada (May 2007)
10
2. Alpaydin, E.: Introduction to machine learning. MIT press (2004)
3. Amgoud, L., Cayrol, C.: A reasoning model based on the production of accept-
able arguments. Annals of Mathematics and Artificial Intelligence 34(1-3), 197–215
(Mar 2002)
4. Antoniou, G., Antoniou, G., Billington, D., Governatori, G., Maher, M.: On the
modelling and analysis of regulations. In: In Proceedings of the Australian Con-
ference on Information Systems (1999)
5. Artz, D., Gil, Y.: A survey of trust in computer science and the semantic web. J.
of Web Semantics 5(2), 58–71 (2007)
6. Athan, T., Boley, H., Governatori, G., Palmirani, M., Paschke, A., Wyner, A.: OA-
SIS LegalRuleML. In: Francesconi, E., Verheij, B. (eds.) International Conference
on Artificial Intelligence and Law, ICAIL ’13. pp. 3–12. ACM (2013)
7. Baroni, P., Cerutti, F., Giacomin, M., Guida, G.: AFRA: Argumentation frame-
work with recursive attacks. International Journal of Approximate Reasoning
52(1), 19–37 (Jan 2011)
8. Bicchieri, C., Muldoon, R.: Social norms. In: Zalta, E.N. (ed.) The Stanford En-
cyclopedia of Philosophy. The Metaphysics Research Lab, Stanford University,
Stanford, CA, USA, spring 2011 edn. (2011)
9. Brams, S., Kilgour, D., Zwicker, W.: The paradox of multiple elections. Social
Choice & Welfare 15(2), 211–236 (1998)
10. Conitzer, V., Lang, J., Sandholm, T.: How many candidates are needed to make
elections hard to manipulate? In: Proceedings of the 9th Conference on Theoretical
Aspects of Rationality and Knowledge. pp. 201–214. ACM (2003)
11. Correia, M., Cruz, J., Leite, J.: On the efficient implementation of social abstract
argumentation. In: Schaub, T., Friedrich, G., O’Sullivan, B. (eds.) 21st European
Conference on Artificial Intelligence, ECAI 2014. Frontiers in Artificial Intelligence
and Applications, vol. 263, pp. 225–230. IOS Press (2014)
12. Criado, N., Argente, E., Noriega, P., Botti, V.J.: MaNEA: A distributed architec-
ture for enforcing norms in open MAS. Eng. Appl. of AI 26(1), 76–95 (2013)
13. Damianou, N.C., Bandara, A.K., Sloman, M.S., Lupu, E.C.: A survey of policy
specification approaches. Tech. rep., Department of Computing, Imperial College
of Science Technology and Medicine, London, UK (2002)
14. Dignum, F., Kinny, D., Sonenberg, L.: From desires, obligations and norms to
goals. Cogn. Sci. Quarterly 2, 407–430 (2002)
15. Dignum, V., Meyer, J.J., Weigand, H.: Towards an organizational model for agent
societies using contracts. In: Proceedings of AAMAS 2002. pp. 694–695. ACM,
New York, NY, USA (2002)
16. d’Inverno, M., Luck, M., Noriega, P., Rodriguez-Aguilar, J.A., Sierra, C.: Commu-
nicating open systems. Artificial Intelligence 186, 38–94 (Jul 2012)
17. Dung, P.M.: On the acceptability of arguments and its fundamental role in non-
monotonic reasoning, logic programming and n-person games. Artificial Intelligence
77(2), 321–357 (Sep 1995)
18. Dunne, P.E., Hunter, A., McBurney, P., Parsons, S., Wooldridge, M.: Weighted
argument systems: Basic definitions, algorithms, and complexity results. Artificial
Intelligence 175(2), 457–486 (Feb 2011)
19. Endriss, U., Maudet, N., Sadri, F., Toni, F.: Negotiating socially optimal alloca-
tions of resources. Journal of Artificial Intelligence Research 25(1), 315–348 (Mar
2006)
20. Foukia, N.: IDReAM: Intrusion detection and response executed with agent mobil-
ity architecture and implementation. In: Proceedings of AAMAS 2005. pp. 264–270.
ACM (2005)
11
21. Gaertner, D., Garcia-Camino, A., Noriega, P., Rodriguez-Aguilar, J.A., Vasconce-
los, W.: Distributed norm management in regulated multiagent systems. In: Pro-
ceedings of the 6th International Joint Conference on Autonomous Agents and
Multiagent Systems. pp. 90:1–90:8. AAMAS ’07, ACM, New York, NY, USA (2007)
22. Grandison, T., Sloman, M.: A survey of trust in internet applications. IEEE Com-
mun. Surveys Tuts. 4(4), 2–16 (2000)
23. Griffiths, N., Luck, M.: Norm Emergence in Tag-Based Cooperation. In: Proceed-
ings of COIN (2010)
24. Horling, B., Lesser, V.: A survey of multi-agent organizational paradigms. Knowl.
Eng. Rev. 19, 281–316 (2004)
25. Leite, J.a., Martins, J.a.: Social abstract argumentation. In: Proceedings of the
Twenty-Second International Joint Conference on Artificial Intelligence. pp. 2287–
2292. IJCAI’11, AAAI Press (2011)
26. Marzo Serugendo, G., Gleizes, M.P., Karageorgos, A.: Self-organising systems. In:
Di Marzo Serugendo, G., Gleizes, M.P., Karageorgos, A. (eds.) Self-organising Soft-
ware, pp. 7–32. Natural Computing Series, Springer (2011)
27. Marzo Serugendo, G., Karageorgos, A., Rana, O., Zambonelli, F.: Engineering Self-
Organising Systems, Lecture Notes in Computer Science, vol. 2977. Springer Berlin
Heidelberg (2004)
28. Modgil, S., Faci, N., Meneguzzi, F., Oren, N., Miles, S., Luck, M.: A framework
for monitoring agent-based normative systems. In: Proceedings of The 8th Inter-
national Conference on Autonomous Agents and Multiagent Systems - Volume 1.
pp. 153–160. AAMAS ’09, International Foundation for Autonomous Agents and
Multiagent Systems, Richland, SC (2009)
29. Morales, J., López-Sánchez, M., Esteva, M.: Using Experience to Generate New
Regulations. In: IJCAI. pp. 307–312 (2011)
30. Morales, J., Lopez-Sanchez, M., Rodriguez-Aguilar, J.A., Wooldridge, M., Vas-
concelos, W.: Automated synthesis of normative systems. In: AAMAS 2013. pp.
483–490 (2013)
31. Pacheco, O., Carmo, J.: A role based model for the normative specification of
organized collective agency and agents interaction. Autonomous Agents and Multi-
Agent Systems 6, 145–184 (March 2003)
32. Rahwan, I., Simari, G.R.: Argumentation in Artificial Intelligence. Springer, 1st
edn. (2009)
33. Ramchurn, S.D., Huynh, D., Jennings, N.R.: Trust in multi-agent systems. Knowl-
edge Engineering Review 19(1), 1–25 (2004)
34. Resnick, P., Zeckhauser, R.: Trust among strangers in Internet transactions: Em-
pirical analysis of eBay’s reputation system. In: Baye, M.R. (ed.) The Economics
of the Internet and E-Commerce, pp. 127–157. Elsevier Science (2002)
35. Robertson, D.: A lightweight method for coordination of agent oriented web ser-
vices. In: Proceedings of AAAI Spring Symposium on Semantic Web Services.
Stanford (2004)
36. Sabater, J., Sierra, C.: Review on computational trust and reputation models. AI
Review 23(1), 33–60 (September 2005)
37. Sen, O., Sen, S.: Effects of social network topology and options on norm emergence.
In: Proceedings of COIN. pp. 211–222 (2010)
38. Sen, S., Airiau, S.: Emergence of norms through social learning. In: IJCAI. pp.
1507–1512 (2007)
39. Shiffman, R.N., Michel, G., Krauthammer, M., Fuchs, N.E., Kaljurand, K., Kuhn,
T.: Writing clinical practice guidelines in controlled natural language. In: Proceed-
12
ings of the 2009 Conference on Controlled Natural Language. pp. 265–280. CNL’09,
Springer-Verlag, Berlin, Heidelberg (2010)
40. Shoham, Y., Tennenholtz, M.: On social laws for artificial agent societies: off-line
design. Artif. Intell. 73, 231–252 (1995)
41. Sloman, M.: Policy driven management for distributed systems. Journal of Network
and Systems Management 2, 333–360 (1994)
42. Teacy, W., Chalkiadakis, G., Rogers, A., Jennings, N.R.: Sequential decision mak-
ing with untrustworthy service providers. In: Proceedings of the 7th International
Conference on Autonomous Agents and Multiagent Systems (2008)
43. Thagard, P.: Coherence in thought and action. MIT Press (2002)
44. Thakoor, S., Morookian, J.M., Chahl, J., Hine, B., Zornetzer, S.: BEES: Exploring
mars with bioinspired technologies. Computer 37(9), 38–47 (2004)
45. Veloso, M.M.: Planning and learning by analogical reasoning, vol. 886. Springer
(1994)
46. Villatoro, D., Sabater-Mir, J., Sen, S.: Social instruments for robust convention
emergence. In: IJCAI. pp. 420–425 (2011)
47. Viroli, M., Zambonelli, F.: A biochemical approach to adaptive service ecosystems.
Inf. Sci. 180(10), 1876–1892 (2010)
48. Wedde, H.F., Farooq, M.: A comprehensive review of nature inspired routing al-
gorithms for fixed telecommunication networks. Journal of Systems Architecture
52(8), 461–484 (Aug 2006)
49. Wilks, Y.: An artificial intelligence approach to machine translation. Springer
(2009)
50. Wyner, A., Bos, J., Basile, V., Quaresma, P.: An empirical approach to the seman-
tic representation of laws. In: Schäfer, B. (ed.) Legal Knowledge and Information
Systems - JURIX 2012: The Twenty-Fifth Annual Conference. Frontiers in Artifi-
cial Intelligence and Applications, vol. 250, pp. 177–180. IOS Press (2012)
51. Wyner, A., van Engers, T., Bahreini, K.: From policy-making statements to first-
order logic. In: Proceedings of the First International Conference on Electronic
Government and the Information Systems Perspective. pp. 47–61. EGOVIS’10,
Springer-Verlag, Berlin, Heidelberg (2010)
52. Wyner, A., Governatori, G.: A study on translating regulatory rules from natural
language to defeasible logic. In: RuleML 2013. pp. 16.1–16.8. Seatle, USA (July
2013)
53. Wyner, A., Peters, W.: On rule extraction from regulations. In: Atkinson, K. (ed.)
Legal Knowledge and Information Systems - JURIX 2011: The Twenty-Fourth
Annual Conference. Frontiers in Artificial Intelligence and Applications, vol. 235,
pp. 113–122. IOS Press (2011)
54. Yin, X., Han, J., Yu, P.S.: Truth discovery with multiple conflicting information
providers on the Web. In: Proceedings of the Conference on Knowledge and Data
Discovery (2007)