Download Norms and artificial agents

Norms and artificial agents
Harko Verhagen
Department of Computer and Systems Sciences
The Royal Institute of Technology and Stockholm University
Forum 100, SE-16440 Kista, Sweden
[email protected]
November 28, 2001
In this section I will first describe the various uses of the word “norm” in
different scientific arenas before stating the view I use. After this I will take
a closer look at the work on norms I have conducted for this thesis. But first
let us once again take a look at the Webster online dictionary:
1. an authoritative standard (model)
2. a principle of right action binding upon the members of a group and
serving to guide, control, or regulate proper and acceptable behavior
3. average as:
(a) a set standard of development or achievement usually derived from
the average or median achievement of a large group
(b) a pattern or trait taken to be typical in the behavior of a social
group
(c) a widespread practice, procedure, or custom (rule)
These different views on norms are reflected in the different views on norms
in different scientific disciplines. For my purposes I will describe the views
on norms in legal theory, (social) psychology, (social) philosophy, sociology,
and decision theory. Where appropriate I will also describe different views
even within these disciplines. After the presentation of these different views,
a working definition is presented and the learning of norms is addressed.
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0.1
Norms in Social Theory
In the description of the normative action model, Habermas [Hab84] identifies
the use of norms in human action patterns as normatively regulated action.
The central concept of complying with a norm means fulfilling a
generalized expectation of behavior. The latter does not have the
cognitive sense of expecting a predicted event, but the normative
sense that members are entitled to expect a certain behavior.
This normative model of action lies behind the role theory that
is widespread in sociology ([Hab84] p.85, original emphasis).
This view is in agreement with Tuomela [Tuo95], who distinguishes two kinds
of social norms (meaning community norms), viz. rules (r-norms) and proper
social norms (s-norms). Rules are norms created by an authority structure
and are always based on agreement-making. Proper social norms are based
on mutual belief. Rules can be formal, in which case they are connected to
formal sanctions, or informal where the sanctions are also informal. Proper
social norms consist of conventions, which apply to a large group such as a
whole human society or socio-economic class, and group-specific norms. The
sanctions connected to both types of proper social norms are social sanctions,
and may include punishment by others and expelling from the group. Aside
from these norms, Tuomela also describes personal norms and also potential
social norms (these are norms which are normally widely obeyed but which
are not in their essence based on “social responsiveness” and which in principle could be personal only). These potential social norms contain among
others moral and prudential norms (m-norms and p-norms respectively). The
reasons for accepting norms differ as to the kind of norms:
• rules are obeyed since they are agreed upon
• proper social norms are obeyed since others expect one to obey
• moral norms are obeyed because of one’s conscience
• prudential norms are obeyed because it is the rational thing to do
The motivational power of all types of norms depends on the norm being a
subject’s reason for action. In other words, norms need to be “internalized”
and “accepted”.
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0.2
Norms in Legal Theory
Within deontic logic, a norm is viewed as an expression of the obligations and
rights connected to the role an individual has within a larger social system.
This is the second of the definitions taken from the Webster dictionary. The
legal theory view on norms corresponds with Tuomela’s r-norms and are
backed by formal sanctions. The different schools in legal theory do not
differ on the definition of a norm but do differ on the mental dimensions of
norms, i.e. on why agents accept and obey norms. In [CFS99] an overview of
these different views is presented. The following reasons for norm accepting
and obeying are given:
• norms are accepted out of fear for the authority issuing the norm
• norms are accepted since they are rational
• norms are accepted from a sense of duty
• norms are accepted since they solve problems of coordination and cooperation
I will leave it to the reader to depict these upon the reasons for obeying as
developed by Tuomela and described in the previous subsection.
0.3
Multiagent Systems Research and Norms
The use of norms in artificial agents is a fairly recent development in multiagent systems research (c.f. e.g., [ST92], [VS97], [Bom99]). Even within
multiagent systems research different definitions of norms are used. In [CC95]
(pp. 91-92) the following views on norms in multiagent system research are
described:
• norms as constraints on behavior
• norms as ends (or goals)
• norms as obligations
Most research on norms in multiagent systems focuses on norms as constraints on behavior via social laws (c.f. e.g. [BC95], [Mal96], [ST92]). These
social laws are designed off-line1 and agents are not allowed to deviate from
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In a recent article [ST97], social laws and conventions are not designed off-line but
emerge at runtime. Social conventions limit the agent’s set of choices to exactly one. The
agents are not allowed to deviate from the social laws or conventions. Furthermore, a
central authority forces agents to comply.
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the social laws (except in the work by Briggs, see below). In this sense the social laws are even more strict than the r-norms Tuomela describes which come
closest to these social laws. The social laws are designed to avoid problems
caused by interacting autonomous selfish agents, thus improving cooperation
and coordination by constraining the agents’ action choices. This view on
norms is based on the view on norms as developed within game theoretical
research such as [UM77]. In [BC95], agents may choose less restrictive sets of
social laws if they can not find a solution under a set of social laws, thus introducing a possibility for deviation. This approach is close to the approach in
[Bom99] where sets of norms are used by an artificial agent decision support
system (pronouncer ) to reorder decision trees with the agent having the possibility to refrain from using the reordered decision tree. The reasons behind
this are not further developed in [Bom99], in contrast to [BC95]. However,
the title of Briggs and Cook’s article Flexible social laws is deceiving, it is
not the laws that are flexible, it is the way they are applied. The laws do
not change, it is the agent who decides to apply them or not. The agent is
only allowed to deviate from a social law if it cannot act. Thus the authors
deny that not acting can be a choice and disconnect the choice of applying
a social law from more realistic reasons other than the possibility to act.
Work on cognitive grounded norms is conducted in the group around
Castelfranchi and Conte (c.f., e.g., [CC95], [CCD99], [CFS99]) or in research
inspired by their work (c.f., e.g., [SH99]). In [CCD99] norms are seen as indispensable for fully autonomous agents. The capacity for norm-acceptance
is taken to depend upon the ability to recognize norms, normative authorities and on solving conflicts among norms. Since normative authorities are
only of importance in the case of r-norms, the agents should also be able
to recognize group members to be able to deal with s-norms. In [Tuo95] a
theory solving conflicts among norms of different categories is developed that
can complement the research described in [CCD99]. The origins of norms is
not clarified in [CCD99]. However, the possibility of norm deviation is an
important addition to multiagent systems research on norms.
0.4
Working Definition of Norms
In the current work agents are viewed as having personal norms and coalition norms. The coalition norms are subjective, thus every agent has an
individual view on each norm of the coalition. The personal norms emerge
from the interaction with the environment. The coalition norms emerge from
interaction with the other agents. This synchronization process will result in
coalition norms that are shared by all agents and will lie somewhere between
the set of personal norms of the individual agents comprising the coalition
REFERENCES
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(this averaging type of norm evolution is commonplace in human subjects
[Bro90]). This final state will only be reached if the group does not change
and if the individual norms have stabilized.
0.5
Learning of Norms
The learning of norms can be divided in two types, viz. the emergence
of norms (c.f. e.g. [UM77] for a description of the emergence of norms
from a game theory point of view) and the acceptance of norms. These
two types of learning express learning at different levels. The emergence of
norms is learning at the level of the social system while the acceptance of
norms is learning at the level of the individual agent. Reasons for accepting
norms are discussed in the above subsection on Tuomela and the subsection
on norms in legal theory. In [CCD99] reasons for the acceptance of norms
in multiagent systems are discussed. I am not primarily interested in why
agents accept norms. Instead I focus on how acceptance of norms changes
the decision making behavior of the agents by changing the agent’s definition
of the norms of the coalition (norm-spreading) and by the adaption of the
agent’s own norms (norm-internalizing).
References
[BC95]
W. Briggs and D. Cook. Flexible Social Laws. In Proceedings of
the 1995 International Joint Conferences on Artificial Intelligence,
pages 688–693. Morgan Kaufmann, 1995.
[Bom99] M. Boman. Norms in Artificial Decision Making. Artificial Intelligence and Law, 7(1):17–35, 1999.
[Bro90]
R. Brown. Group Processes: Dynamics Within and Between
Groups. Basil Blackwell, Cambridge, MA., 1990.
[CC95]
R. Conte and C. Castelfranchi. Cognitive and social action. UCL
Press London, 1995.
[CCD99] R. Conte, C. Castelfranchi, and F. Dignum. Autonomous NormAcceptance. In Intelligent Agent V: Proceedings of ATAL 98, 1999.
[CFS99] R. Conte, R. Falcone, and G. Sartor. Introduction: Agents and
Norms: How to Fill the Gap? Artificial Intelligence and Law,
pages 1–15, 1999.
REFERENCES
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[Hab84] J. Habermas. The Theory of Communicative Action, Volume One,
Reason and the Rationalization of Society. Beacon Press, Boston,
1984. transl McCarthy, orig publ as Theorie des Kommunikativen
Handels, 1981.
[Mal96] A.D. Mali. Social Laws for Agent Modeling. In M. Tambe and
P. Gmytrasiewicz, editors, Agent Modeling Papers from the AAAI
Workshop, pages 53–60. AAAI Press, 1996.
[SH99]
N.J. Saam and A. Harrer.
Simulating Norms, Social Inequality, and Functional Change in Artificial Societies. Journal of Artificial Societies and Social Simulation, 2(1), 1999.
www.soc.surrey.ac.uk/JASSS/2/1/2.html.
[ST92]
Y. Shoham and M. Tennenholtz. On the Synthesis of Useful Social Laws for Artificial Agent Societies (Preliminary Report). In
Proceedings of the National Conference on Artificial Intelligence,
pages 276–281, San Jose, CA, July 1992.
[ST97]
Y. Shoham and M. Tennenholtz. On the Emergence of Social Conventions: modeling, analysis, and simulations. Artificial Intelligence, 94(1-2):139–166, 1997.
[Tuo95] R. Tuomela. The Importance of Us: A Philosophical Study of Basic
Social Norms. Stanford University Press, 1995.
[UM77]
E. Ullman-Margalit. The Emergence of Norms. Clarendon Press,
1977.
[VS97]
H.J.E. Verhagen and R.A. Smit. Multiagent Systems as Simulation
Tools for Social Theory Testing. Paper presented at poster session
at ICCS and SS Siena, 1997.