Agents, Power and Norms Michael Luck, Fabiola López y López University of Southampton, UK...
Transcript of Agents, Power and Norms Michael Luck, Fabiola López y López University of Southampton, UK...
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Agents, Power and Norms
Michael Luck, Fabiola López y LópezUniversity of Southampton, UKBenemérita Universidad Autonoma de Puebla, Mexico
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Part I
Michael Luck, Fabiola López y LópezUniversity of Southampton, UKBenemérita Universidad Autonoma de Puebla, Mexico
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Research Motivations
Agents have limited capabilities The capabilities of others are needed to
succeed Agents are autonomous Benevolence cannot be taken for
granted Power can be used to influence agents Powers are neither eternal nor absolute
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Research Motivations
Agents and Societies
Societies achieve social order through norms.
Agents must have a model of societies.
Agents must be able to recognise normative relationships.
Norms are dynamic concepts.
Agents must be aware of the changes due to norms.
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Research Motivations
Societies and Autonomous Agents. How can autonomous agents be
integrated into societies regulated by norms?
What does an agent need to deal with norms?
What does an agent evaluate before dismissing a norm?
How are the goals of an agent affected by social regulations?
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Overview
Autonomous Agents
Normative Multi-Agent Systems
Institutional Powers
Personal Powers
Conclusions
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Aims
General: To build a framework to represent agents able
to exist in a society in which social order is achieved through norms.
Particular: To provide a basic representation of norm-
based systems. To analyse the dynamics of norms. To describe different kinds of normative
relationships that agents might use in decision-making processes.
To identify powers in a society. To identify personal powers of agents.
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Overview
Norms and Normative Agents
Normative Multi-Agent Systems
Dynamics of Norms
Norm Relationships
Conclusions
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Multi-Agent Systems
Formal model based on Luck and d’Inverno’s SMART agent framework.
Autonomous agents are essentially defined in terms of their capabilities, goals, beliefs and motivations.
Multi-agent systems are collections of agents from which at least one is autonomous.
Multi-agent systems cannot exist without some interaction among their members.
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Normative Agents
A normative agent is an autonomous agent whose behaviour is shaped by the norms it must comply with.
A normative agent must be
able to decide, based on its own goals and motivations, whether a norm must be either adopted or complied with
aware of the consequences of dismissing norms.
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Normative Multi-Agent Systems A normative multi-agent system is a
collection of normative agents which are controlled by a set of common norms varying from obligations and social commitments, to social codes.
Normative multi-agent systems are characterised by the membership of some agents, the norms that members are expected to comply
with, norms to enforce and encourage other norms, and norms to legislate.
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Normative Systems: Membership Autonomous agents join societies as a way to
satisfy goals whose success relies on the actions of other agents.
Members recognise themselves as part of the society by adopting some of its norms.
Agents can be part of more than one society. Compliance with norms is never taken for granted. Enforcement and encouragement of norms are
needed. Addressees of norms must be members of the
system.
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Normative Multi-Agent Systems Disorder and conflicts of interest might appear
• when norms must be changed, and when punishments and rewards must be applied.
These faculties are restricted to specific sets of agents through special sets of norms.
These norms specify how some agents have to behave when norms must be changed, or norm becomes either fulfilled or unfulfilled.
Fulfilment of norms is achieved when the corresponding normative goals become satisfied.
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Normative Roles
From the different kinds of norms in a system, normative roles for agents can be identified.
Legislators (addressees of legislation norms)
Defenders (addressees of either enforcement or reward norms)
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Dynamics of NormsIssue Spread
Adoption
Activation
Reward
Compliance
Violation
Modification
Abolition
Sanction Non-sanction
Dismissal
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Legislation norms
legislators membersRelations of authority
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Active norms
defenders
addressees beneficiaries
Enforcement
relations
Relations of responsibility
Relations of benefit
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Fulfilled Norms
defenders
addressees beneficiaries
Entitled to give
rewards
Right to claim
rewards
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Violated Norms
defenders
addressees beneficiaries
Entitled
to punish
Relations of deception
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Norm Relationships
Norm relationships can be used by agents to:
To determine empowered situations of agents.
To find reasons to adopt and comply with norms.
To find reasons to provide help.
To take advantage of social benefits in order to satisfy their goals.
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Z Specification
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Z Specification
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Conclusions
This work gives the means for agents to reason about norms by providing: A formal structure of norms that includes the
different elements that must be taken into account when reasoning about norms.
A formal basic representation of norm-based systems.
An analysis and formalisations of the basic kinds of norms that norm-based systems have.
An analysis of the dynamics of norms. The set of normative relationships that might
emerge by adopting, complying and dismissing norms.
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Part II
Michael Luck, Fabiola López y LópezUniversity of Southampton, UKBenemérita Universidad Autonoma de Puebla, Mexico
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Autonomous Agents
Formal model based on Luck and d’Inverno’s SMART agent framework.
Autonomous agents are essentially defined in terms of their capabilities, goals, beliefs and motivations.
Interaction among agents results from one agent satisfying the goals of another.
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Normative Multi-Agent Systems
Norms are mechanisms that a society has in order to influence the behaviour of agents.
Categories of Norms:
Obligations Prohibitions
Social Commitments Social Codes
A normative agent is an autonomous agent whose behaviour is shaped by the norms it must comply with (AAMAS’02)
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Normative Multi-Agent Systems
Norm Structure
Normative Goals
Addressees
Context
Exceptions
Beneficiaries
Rewards
Punishments
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Normative Multi-Agent Systems
Normative multi-agent system model (RASTA’02 at AAMAS’02)
Members
System norms
Legislation norms
Enforcement norms
Reward norms
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Normative Multi-Agent Systems
Legislation norms allow some agents to create, modify, and abolish the norms of the system.
Issue and abolition of norms permitted
Legislation norm
normative goals punishmentscontext rewards legislators . . .
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Normative Multi-Agent Systems
Enforcement norms are norms which specify what kinds of punishments must be applied when norms are unfulfilled, and who is responsible for the punishment.
unsatisfied normative goals
Norm normative goals punishmentscontext rewards addressees . . .
Enforcement norm
normative goals punishmentscontext rewards defenders . . .
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Normative Multi-Agent Systems Reward norms are norms to specify
who is responsible for rewards due to norm compliance.
satisfied normative goals
Norm normative goals punishmentscontext rewards addressees . . .
Reward norm
normative goals punishmentscontext rewards defenders . . .
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Institutional Powers
Legislation norms
legislators membersLegal Power
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Institutional Powers
Reward norms
defendersaddresseesLegal Reward Power
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Institutional Powers
Enforcement norms
defenders addresseesLegal Coercive Power
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Institutional Powers
System norms
beneficiaries addresseesLegal Benefit Power
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Personal Powers
Agent capabilities to satisfy goals
Ag satisfy (g1)
benefit
s
Ag (g2)
hindersAg
(g3) Illegal Coercive Power
Ag (g3)
Ag satisfy (g1)
Facilitation Power
Ag (g2)
Ag satisfy (g1)
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Personal Powers
Agent benevolence towards a group of agents
Comrade Power
Ag satisfy (g1)Ag
(g2)Facilitation
Power
Ag satisfy (g1)
comrades
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Personal Powers
Agent rewarded by past actions
Facilitation Power
Ag (g2)
Ag satisfy (g1)
Reciprocation Power
Ag (g2)
Ag satisfy (g1)
Fulfilled Norm Benefits
Ag (g2) Ag satisfy (g1)
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Personal Powers
Agents exchange goals
Facilitation Power
Ag (g2)
Ag satisfy (g1)
Exchange Power
Ag (g2)
Ag (g4)
Facilitation Power
Ag (g4)
Ag satisfy (g3)Exchange Power
Ag (g4)
Ag (g2)
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Z Specification
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Conclusions
This work gives the means for agents to identify power in their current situations of powers in which they are.
Uses a formal model of systems regulated by norms.
Analyses powers due to the role agents play in a society.
Analyses powers due to an agent’s capabilities.
Provides a taxonomy of powers.
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Part III
Michael Luck, Fabiola López y LópezUniversity of Southampton, UKBenemérita Universidad Autonoma de Puebla, Mexico
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Research Motivations
Societies and Autonomous Agents.
How can autonomous agents be integrated into societies regulated by norms?
What does an agent need to deal with norms?
What does an agent evaluate before dismissing a norm?
How are the goals of an agent affected by social regulations?
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Overview
Norms and Normative Agents
The Norm Compliance Process
Strategies for Norm Compliance
Experiments with Normative Agents
Conclusions and Additional Work
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Norms and Normative Agents
Norm adoption is the process through which an agent decides to create an internal representation of a norm.
Norm compliance is the process through which an agent’s goals are updated according to the norms it has decided to comply with.
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Norms and Normative Agents A normative agent is an autonomous
agent whose behaviour is shaped by the norms it must comply with.
A normative agent must be
able to decide, based on its own goals and motivations, whether a norm must be either adopted or complied with.
aware of the consequences of dismissing norms.
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Norms and Normative Agents Compliance with norms is
enforced through punishments, and
encouraged through rewards.
Neither punishments nor rewards are effective without being related to the current goals of an agent.
Punishments must hinder important goals.
Rewards must benefit important goals.
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Norm Compliance: norm processing
normsactive norms
intended norms
rejected norms
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Norm Compliance: affected goals
normative goals
hindered by
normative gs
rewardsbenefited
fromrewards
intended norms
punishmentshindered
bypunishments
rejected norms
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Norm Compliance: updating goals
currentgoals
goals normative goals
hindered by
normative gs
benefited from
rewards
hindered by
punishments
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Strategies for Norm Compliance Social
All norms are complied with.
Rebellious All norms are rejected.
Fearful A norm including
punishments is always complied with.
Greedy A norm including rewards
is always complied with.
norm with punishment
norm
intendednorms
norm with reward
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Analysis of active norms
active norms
hindered by
normative gs =
non-conflicting
norms
hindered by
normative gs
conflictingnorms
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Pressured Strategy
Non-conflicting norms are complied with if their punishments hinder any existing goal.
intendednorms
nonconflicting
norms
=
hindered by
punishments
hindered by
normative gs
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Pressured Strategy
Conflicting norms are complied with if the goals hindered by punishments are more important than the goals hindered by normative goals.
intendednorms
conflictingnorms
hindered by
normative gs
hindered by
punishments >hindered
bynormative gs
hindered by
punishments
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Opportunistic Strategy
Non-conflicting norms are complied with if the offered rewards might benefit a goal.
intendednorms
nonconflicting
norms
hindered by
normative gs =
benefited from
rewards
![Page 56: Agents, Power and Norms Michael Luck, Fabiola López y López University of Southampton, UK Benemérita Universidad Autonoma de Puebla, Mexico.](https://reader036.fdocuments.in/reader036/viewer/2022062715/56649d835503460f94a68b8b/html5/thumbnails/56.jpg)
Opportunistic Strategy
Conflicting norms are complied with if associated rewards benefit more important goals than those that might be hindered by normative goals.
intendednorms
conflictingnorms
hindered by
normative gs
benefited from
rewards
>benefited
fromrewards
hindered by
normative gs
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Z Specification
![Page 58: Agents, Power and Norms Michael Luck, Fabiola López y López University of Southampton, UK Benemérita Universidad Autonoma de Puebla, Mexico.](https://reader036.fdocuments.in/reader036/viewer/2022062715/56649d835503460f94a68b8b/html5/thumbnails/58.jpg)
Z Specification
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Experiments with Normative Agents
Agent Strategies for non conflicting norms
Strategies for conflicting norms
Social Social Social
Rebellious Rebellious Rebellious
Selfish Pressured & Opportunistic
Pressured & Opportunistic
Social-SelfishSocial
Pressured & Opportunistic
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Experiments with Normative Agents Individual performance is the proportion of personal
goals that become satisfied under the presence of norms.
Social contribution represents the proportion of norms complied with by an agent who has its own goals.
Experiments were run
by varying the number of conflicts between the goals of an agent and the normative goals of the corresponding norms (from 0% to 100%), and
by taking different sizes for the sets of current goals and active norms.
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Experiments with Normative Agents Internal and external conditions were similar for all
agents.
Agents have similar goals
Similar norms become active at the same time.
The importance of each goal is also the same for all agents.
Complete social control was assumed.
All punishments were applied.
All offered rewards were given.
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Experiments with Normative Agents
Social
0
0.2
0.4
0.6
0.8
1
1.2
0% 25% 50% 75% 100%
IP
SC
Rebellious
0
0.2
0.4
0.6
0.8
1
1.2
0% 25% 50% 75% 100%
IP
SC
Selfish
0
0.2
0.4
0.6
0.8
1
1.2
0% 25% 50% 75% 100%
IP
SC
SocialSelf
0
0.2
0.4
0.6
0.8
1
1.2
0% 25% 50% 75% 100%
IP
SC
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Conclusions
A formal structure of norms that includes the different elements that must be taken into account when reasoning about norms.
A formal model to incorporate the process of norm-compliance into a BDI-like agent architecture.
A set of strategies that agents might follow to decide when norms must be complied with.
Different ways to combine strategies to define complex normative behaviours.
An analysis of normative agent behaviour when total social control is exerted.