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1. Computational Trust and Reputation Models
3. Presentation index Motivation
Approaches to control de interaction
Some definitions
The computational perspective
4. Motivation
5. Let’s start introducing what we are talking about.
Niklas Luhman, a well known German Sociologist said...
I’m not saying anything unknwon if I say that Trust and trusworthiness are necessary in our everyday life. It is part of the “glue” that holds our society together.
Similarly reputation is a social artifact that has been present in our societies for a long time.
From ancient Greeks to modern days, from Vietnamese to Bedouins, the concept of reputation plays a very important role in human social organizations.Let’s start introducing what we are talking about.
Niklas Luhman, a well known German Sociologist said...
I’m not saying anything unknwon if I say that Trust and trusworthiness are necessary in our everyday life. It is part of the “glue” that holds our society together.
Similarly reputation is a social artifact that has been present in our societies for a long time.
From ancient Greeks to modern days, from Vietnamese to Bedouins, the concept of reputation plays a very important role in human social organizations.
13. Advantages of trust and reputation mechanisms Each agent is a norm enforcer and is also under surveillance by the others. No central authority needed.
Their nature allows to arrive where laws and central authorities cannot.
Punishment is based usually in ostracism.
14. Problems of trust and reputation mechanisms Bootstrap problem.
Exclusion must be a punishment for the outsider.
Not all kind of environments are suitable to apply these mechanisms.
15. Approaches to control the interaction
16. Different approaches to control the interaction Second slide: security approach : we can deploy a security infrastructure that constraint the behaviour of agents. These infratstructure (such as PKI) guarantee some properties, essentially during communication (authentication, confidentiality, non-repudation, integrity, ...). The problem is that these approach do not cover all the risks. Mainly it does not refer to the _content_ of messages and it cannot guarantee that an agent tells the truth or that it will behave as it committed to do.Second slide: security approach : we can deploy a security infrastructure that constraint the behaviour of agents. These infratstructure (such as PKI) guarantee some properties, essentially during communication (authentication, confidentiality, non-repudation, integrity, ...). The problem is that these approach do not cover all the risks. Mainly it does not refer to the _content_ of messages and it cannot guarantee that an agent tells the truth or that it will behave as it committed to do.
17. Security approach Different approaches to control the interaction Second slide: security approach : we can deploy a security infrastructure that constraint the behaviour of agents. These infratstructure (such as PKI) guarantee some properties, essentially during communication (authentication, confidentiality, non-repudation, integrity, ...). The problem is that these approach do not cover all the risks. Mainly it does not refer to the _content_ of messages and it cannot guarantee that an agent tells the truth or that it will behave as it committed to do Second slide: security approach : we can deploy a security infrastructure that constraint the behaviour of agents. These infratstructure (such as PKI) guarantee some properties, essentially during communication (authentication, confidentiality, non-repudation, integrity, ...). The problem is that these approach do not cover all the risks. Mainly it does not refer to the _content_ of messages and it cannot guarantee that an agent tells the truth or that it will behave as it committed to do
18. Different approaches to control the interaction Third slide: institutionnal approach : here the agents are deployed over a specific infrastructure called institution. This institution has some power over the agents : it can observe their behaviour and also has a sanctionning power. Then it can punish agents that do not behave well. This approach tackles the problem of controlling the agents' behaviour but it makes some strong assumption. First it requires that the institution
can observe all what is going on in the agent society (not feasable in decentralized systems). Second, it is an intrusive approach that needs that the institution can sanction agents. Third slide: institutionnal approach : here the agents are deployed over a specific infrastructure called institution. This institution has some power over the agents : it can observe their behaviour and also has a sanctionning power. Then it can punish agents that do not behave well. This approach tackles the problem of controlling the agents' behaviour but it makes some strong assumption. First it requires that the institution
can observe all what is going on in the agent society (not feasable in decentralized systems). Second, it is an intrusive approach that needs that the institution can sanction agents.
19. Institutional approach Different approaches to control the interaction Third slide: institutionnal approach : here the agents are deployed over a specific infrastructure called institution. This institution has some power over the agents : it can observe their behaviour and also has a sanctionning power. Then it can punish agents that do not behave well. This approach tackles the problem of controlling the agents' behaviour but it makes some strong assumption. First it requires that the institution
can observe all what is going on in the agent society (not feasable in decentralized systems). Second, it is an intrusive approach that needs that the institution can sanction agents. Third slide: institutionnal approach : here the agents are deployed over a specific infrastructure called institution. This institution has some power over the agents : it can observe their behaviour and also has a sanctionning power. Then it can punish agents that do not behave well. This approach tackles the problem of controlling the agents' behaviour but it makes some strong assumption. First it requires that the institution
can observe all what is going on in the agent society (not feasable in decentralized systems). Second, it is an intrusive approach that needs that the institution can sanction agents.
20. Different approaches to control the interaction Fourth slide: the social approach. In this approach, each agent can participate to the control of other agents. This is called social control by Castelfranchi & Falcone. There is no need of a global complete view of the systems not of an external intrusive power over agents. Each agent observe its neighborhood, evaluate its neighbours by the way of a reputation model and exchange this information with others. Agents that do not behave well will see their reputations in the other agents' model decrease, and will be progressivly excluded from interactions and then from the society. This approach is suited for large scale decentralized systems, but as it is a kind of learning process, there is usually some time during when malicious agents can function before being discovered. Fourth slide: the social approach. In this approach, each agent can participate to the control of other agents. This is called social control by Castelfranchi & Falcone. There is no need of a global complete view of the systems not of an external intrusive power over agents. Each agent observe its neighborhood, evaluate its neighbours by the way of a reputation model and exchange this information with others. Agents that do not behave well will see their reputations in the other agents' model decrease, and will be progressivly excluded from interactions and then from the society. This approach is suited for large scale decentralized systems, but as it is a kind of learning process, there is usually some time during when malicious agents can function before being discovered.
21. Example: P2P systems - There is some global tasks that can only be achieved by a collective activity of several agents (for instance query routing) - Then part of these global tasks are achieved by agents that have been deployed and/or developped by different users - There is no guarantee that these agents behave well. If they are buggy or malicious, their behaviour can perturb the system or even prevent the accomplishment of global tasks. - Trust and reputations are means to evaluate the compliance of agents behaviour according to some expected behaviour - As p2p systems are fully decentralized, it is not possible to have a complete global view of each agent and to monitor their behaviour. It is the neighborhood of an agent that can do it, using reputation and trust, and then share and propagate the results of their observation - There is some global tasks that can only be achieved by a collective activity of several agents (for instance query routing) - Then part of these global tasks are achieved by agents that have been deployed and/or developped by different users - There is no guarantee that these agents behave well. If they are buggy or malicious, their behaviour can perturb the system or even prevent the accomplishment of global tasks. - Trust and reputations are means to evaluate the compliance of agents behaviour according to some expected behaviour - As p2p systems are fully decentralized, it is not possible to have a complete global view of each agent and to monitor their behaviour. It is the neighborhood of an agent that can do it, using reputation and trust, and then share and propagate the results of their observation
22. Example: P2P systems
23. Example: P2P systems
24. Different approaches to control the interaction Fourth slide: the social approach. In this approach, each agent can participate to the control of other agents. This is called social control by Castelfranchi & Falcone. There is no need of a global complete view of the systems not of an external intrusive power over agents. Each agent observe its neighborhood, evaluate its neighbours by the way of a reputation model and exchange this information with others. Agents that do not behave well will see their reputations in the other agents' model decrease, and will be progressivly excluded from interactions and then from the society. This approach is suited for large scale decentralized systems, but as it is a kind of learning process, there is usually some time during when malicious agents can function before being discovered. Fourth slide: the social approach. In this approach, each agent can participate to the control of other agents. This is called social control by Castelfranchi & Falcone. There is no need of a global complete view of the systems not of an external intrusive power over agents. Each agent observe its neighborhood, evaluate its neighbours by the way of a reputation model and exchange this information with others. Agents that do not behave well will see their reputations in the other agents' model decrease, and will be progressivly excluded from interactions and then from the society. This approach is suited for large scale decentralized systems, but as it is a kind of learning process, there is usually some time during when malicious agents can function before being discovered.
25. Definitions
28. The antrologist Fredrik Barth tells the story of his dealing with a rug merchant in a bazaar in the Middle East. Barth found a rug that he liked but he had no way to pay for it at the time. The dealer told him to take the rug and send the money later.
All of us have had similar situations where we are trusted by a complete stranger who would quite likely never see us again.The antrologist Fredrik Barth tells the story of his dealing with a rug merchant in a bazaar in the Middle East. Barth found a rug that he liked but he had no way to pay for it at the time. The dealer told him to take the rug and send the money later.
All of us have had similar situations where we are trusted by a complete stranger who would quite likely never see us again.
33. Computational perspective
34. Dimensions of trust [McKnight & Chervany, 02]
35. The Functional Ontology of Reputation [Casare & Sichman, 05] The Functional Ontology of Reputation (FORe) aims at defining standard concepts related to reputation
FORe includes:
Reputation processes
Reputation types and natures
Agent roles
Common knowledge (information sources, entities, time)
Facilitate the interoperability of heterogeneous reputation models
36. Processes needed for trust computation Initialisation
first default value
Evaluation
judgement of a behaviour
Punishment/Sanction
calculation of reputation values
Reasoning
inference of trust intentions
Decision
decision to trust
Propagation
communication about reputation/trust information
37. Agent roles
38. Reputation types [Casare & Sichman, 05] Primary reputation
Direct reputation
Observed reputation
Secondary reputation
Collective reputation
Propagated reputation
Stereotyped reputation
39. What is a good trust model ? A good trust model should be [Fullam et al, 05]:
Accurate
provide good previsions
Adaptive
evolve according to behaviour of others
Quickly converging
quickly compute accurate values
Multi-dimensional
Consider different agent characteristics
Efficient
Compute in reasonable time and cost
40. Why using a trust model in aMAS ? Trust models allow:
Identifying and isolating untrustworthy agents
41. Why using a trust model in aMAS ? Trust models allow:
Identifying and isolating untrustworthy agents
Evaluating an interaction’s utility
42. Why using a trust model in aMAS ? Trust models allow:
Identifying and isolating untrustworthy agents
Evaluating an interaction’s utility
Deciding whether and with whom to interact
43. Presentation index Motivation
Approaches to control de interaction
Some definitions
The computational perspective
44. Computational trust and reputation models eBay
TrustNet
LIAR
ReGret
Repage
49. Computational trust and reputation models eBay
TrustNet
LIAR
ReGret
Repage
50. Trust Net [Schillo & Funk, 99] Model designed to evaluate the agents’ honesty
Completely decentralized
Applied in a game theory context : the Iterated Prisonner’s Dilemma (IPD)
Each agent announce its strategy and choose an opponent according to its announced strategy
If an agent does not follow the strategy it announced, its opponent decreases its reputation
The trust value of agent A towards agent B is
T(A,B) = number of honest rounds / number of total rounds
51. Agents can communicate their trust values to fasten the convergence of trust models
An agent can build a Trust Net of trust values transmitted by witnesses
The final trust value of an agent towards another aggregate direct experiences and testimonies with a probabilistic function on the lying behaviour of witnesses Trust Net [Schillo & Funk, 99]
52. Computational trust and reputation models eBay
TrustNet
LIAR
ReGret
Repage
53. The LIAR model [Muller & Vercouter, 07] Model designed for the control of communications in a P2P network
Completely decentralized
Applied to a peer-to-peer protocol for query routings
The global functionning of a p2p network relies on an expected behaviour of several nodes (or agents)
Agents’ behaviour must be regulated by a social control [Castelfranchi, 00]
54. LIAR: Social control of agent communications
55. The LIAR agent architecture
56. Detection of violations
57. Reputation types in LIAR Rptargetbeneficary(facet,dimension,time) ? [-1,+1] ? {unknown}
58. Reputation computation Direct Interaction based Reputation
Separate the social policies according to their state
associate a penalty to each set
reputation = weighted average of the penalties
Reputation Recommendation based Reputation
based on trusted recommendation
reputation = weighted average of received values
weighted by the reputation of the punisher
59. LIAR decision process
60. Computational trust and reputation models eBay
TrustNet
LIAR
ReGret
Repage
64. Outcome:
The initial contract
to take a particular course of actions
to establish the terms and conditions of a transaction.
AND
The actual result of the contract.
66. Impression:
The subjective evaluation of an outcome from a specific point of view. En quč consiteix aquesta avaluació? -> Noció d’utilitat.En quč consiteix aquesta avaluació? -> Noció d’utilitat.
69. Reputation that an agent builds on another agent based on the beliefs gathered from society members (witnesses).
73. Witness reputation
74. Witness reputation
82. The trust on the agents that are in the “neighbourhood” of the target agent and their relation with it are the elements used to calculate what we call the Neighbourhood reputation.
84. The idea behind the System reputation is to use the common knowledge about social groups and the role that the agent is playing in the society as a mechanism to assign reputation values to other agents.
The knowledge necessary to calculate a system reputation is usually inherited from the group or groups to which the agent belongs to.
85. If the agent has a reliable direct trust value, it will use that as a measure of trust. If that value is not so reliable then it will use reputation.
86. Computational trust and reputation models eBay
TrustNet
LIAR
ReGret
Repage
89. The Repage system
99. The analyzer The interplay between image and reputation might be a cause of uncertainty and inconsistency.
Inconsistencies do not necessarily lead to a state of cognitive dissonance, nor do they always urge the system to find a solution.
For example, an inconsistency between own image of a given target and its reputation creates no problem to the system.
However, a contradiction between own evaluations is sometimes possible:
my direct experience may be confirmed in further interaction, but at the same time it may be challenged by the image I believe others, whom I trust a lot, have formed about the same target
What will I do in such a condition? Will I go ahead and sign a contract, may be a low-cost one, just to acquire a new piece of direct evidence, or will I check the reliability of my informants?
The picture is rather complex, and the number of possibilities is bound to increase at any step, making the application of rule-based reasoning computationally heavy.
101. Current work with the RepAge architecture Agents that are able to justify the values of Images and reputations (the LRep language).
Formalization that allows an agent to reason about the elements that conform an image and/or a reputation.
Dynamic ontology mapping.
103. Dmitry Karamazov tells the story of a liuténant who, as commander of a unit far from Moscow, has managed substantial sums of money on behalf of the army.
Immediately after each periodic audit of his books, he was taking the available funds to the merchant Trifonov.
After some time, Trifonov was returning the money with interests.
Because it was highly irregular...
Dmitry Karamazov tells the story of a liuténant who, as commander of a unit far from Moscow, has managed substantial sums of money on behalf of the army.
Immediately after each periodic audit of his books, he was taking the available funds to the merchant Trifonov.
After some time, Trifonov was returning the money with interests.
Because it was highly irregular...
104. When the day comes that the liutenant is abruptly to be replaced in his command, he asks Trifonov to return the last sum loaned to him.
Trifonov replies...When the day comes that the liutenant is abruptly to be replaced in his command, he asks Trifonov to return the last sum loaned to him.
Trifonov replies...
105. This is only an example...there are other situations similar to this.This is only an example...there are other situations similar to this.
112. Comparison among models Slide 11, Guillaume’s thesis
Some slides to show that there are a lot of models and they are quite different.
113. Presentation index Motivation
Approaches to control de interaction
Some definitions
The computational perspective
114. The Agent Reputation and Trust Testbed
115. Motivation Trust in MAS is a young field of research, experiencing breadth-wise growth
Many trust-modeling technologies
Many metrics for empirical validation
Lack of unified research direction
No unified objective for trust technologies
No unified performance metrics and benchmarks
116. An Experimental and Competition Testbed… Presents a common challenge to the research community
Facilitates solving of prominent research problems
Provides a versatile, universal site for experimentation
Employs well-defined metrics
Identifies successful technologies
Matures the field of trust research
Utilizes an exciting domain to attract attention of other researchers and the public
117. The ART Testbed A tool for
Experimentation: Researchers can perform easily-repeatable experiments in a common environment against accepted benchmarks
Competitions: Trust technologies compete against each other; the most promising technologies are identified
118. Testbed Game Rules Agents are art appraisers with varying expertise in different eras
For a fixed price, clients ask appraisers to provide “appraisals” of paintings from various eras.
If an appraiser is not very knowledgeable about a painting, it can purchase “opinions” from other appraisers who might be experts in the respective era
Appraisers whose appraisals are more accurate receive larger shares of the client base in the future
Appraisers compete to achieve the highest earnings by the end of the game.
Agents are art appraisers with varying expertise in different eras
For a fixed price, clients ask appraisers to provide “appraisals” of paintings from various eras.
If an appraiser is not very knowledgeable about a painting, it can purchase “opinions” from other appraisers who might be experts in the respective era
Appraisers whose appraisals are more accurate receive larger shares of the client base in the future
Appraisers compete to achieve the highest earnings by the end of the game.
119. Step 1: Client and Expertise Assignments Appraisers receive clients who pay a fixed price to request appraisals
Client paintings are randomly distributed across eras
As game progresses, more accurate appraisers receive more clients (thus more profit)
120. Step 2: Reputation Transactions Appraisers know their own level of expertise for each era
Appraisers are not informed (by the simulation) of the expertise levels of other appraisers
Appraisers may purchase reputations, for a fixed fee, from other appraisers
Reputations are values between zero and one
Might not correspond to appraiser’s internal trust model
Serves as standardized format for inter-agent communication
121. Step 2: Reputation Transactions
122. Step 3: Opinion Transactions For a single painting, an appraiser may request opinions (each at a fixed price) from as many other appraisers as desired
The simulation “generates” opinions about paintings for opinion-providing appraisers
Accuracy of opinion is proportional to opinion provider’s expertise for the era and cost it is willing to pay to generate opinion
Appraisers are not required to truthfully reveal opinions to requesting appraisers
123. Step 3: Opinion Transactions
124. Step 4: Appraisal Calculation Upon paying providers and before receiving opinions, requesting appraiser submits to simulation a weight (self-assessed reputation) for each other appraiser
Simulation collects opinions sent to appraiser (appraisers may not alter weights or received opinions)
Simulation calculates “final appraisal” as weighted average of received opinions
True value of painting and calculated final appraisal are revealed to appraiser
Appraiser may use revealed information to revise trust models of other appraisers
125. Analysis Metrics Agent-Based Metrics
Money in bank
Average appraisal accuracy
Consistency of appraisal accuracy
Number of each type of message passed
System-Based Metrics
System aggregate bank totals
Distribution of money among appraisers
Number of messages passed, by type
Number of transactions conducted
Evenness of transaction distribution across appraisers
126. Conclusions The ART Testbed provides a tool for both experimentation and competition
Promotes solutions to prominent trust research problems
Features desirable characteristics that facilitate experimentation
127. An example of using ART Building an agent
creating a new agent class
strategic methods
Running a game
designing a game
running the game
Viewing the game
Running a game monitor interface
128. Building an agent for ART An agent is described by 2 files:
a Java class (MyAgent.java)
must be in the testbed.participant package
must extend the testbed.agent.Agent class
an XML file (MyAgent.xml)
only specifying the agent Java class in the following way:
<agentConfig>
<classFile>
c:\ARTAgent\testbed\participants\MyAgent.class
</classFile>
</agentConfig>
129. Strategic methods of the Agent class (1) For the beginning of the game
initializeAgent()
To prepare the agent for a game
For reputation transactions
prepareReputationRequests()
To ask reputation information (gossips) to other agents
prepareReputationAcceptsAndDeclines()
To accept or refuse requests
prepareReputationReplies()
To reply to confirmed requests
130. Strategic methods of the Agent class (2) For opinion transactions
prepareOpinionRequests()
To ask opinion to other agents
prepareOpinionCertainties()
To announce its own expertise to a requester
prepareOpinionRequestConfirmations()
To confirm/cancel requests to providers
prepareOpinionCreationOrders()
To produce evaluations of paintings
prepareOpinionProviderWeights()
To weight the opinion of other agents
prepareOpinionReplies()
To reply to confirmed requests
131. The strategy of this example of agent We will implement an agent with a very simple reputation model:
It associates a reputation value to each other agent (initialized at 1.0)
It only sends opinion requests to agents with reputation > 0.5
No reputation requests are sent
If an appraisal of another agent is different from the real value by less than 50%, reputation is increased by 0.03
Otherwise it is decreased by 0.03
If our agent receives a reputation request from another agent with a reputation less than 0.5, it provides a bad appraisal (cheaper)
Otherwise its appraisal is honest
132. Initialization
133. Opinion requests
134. Opinion Creation Order
135. Updating reputations
136. Running a game with MyAgent Parameters of the game :
3 agents: MyAgent, HonestAgent, CheaterAgent
50 time steps
4 painting eras
average client share : 5 / agent
137. How did my agent behaved ?
138. References [Casare & Sichman, 05] S. J. Casare and J. S. Sichman, Towards a functional ontology of reputation, Proceedings of AAMAS’05, 2005
[Castelfranchi, 00] C. Castelfranchi, Engineering Social Order, Proceedings of ESAW’00, 2000
[Fullam et al, 05] K. Fullam, T. Klos, G. Muller, J. Sabater-Mir, A. Schlosser, Z. Topol, S. Barber, J. Rosenschein, L. Vercouter and M. Voss, A Specification of the Agent Reputation and Trust (ART) Testbed: Experimentation and Competition for Trust in Agent Societies, Proceedings of AAMAS’05, 2005
[McKnight & Chervany, 02] D. H. McKnight and N. L. Chervany, What trust means in e-commerce customer relationship: an interdisciplinary conceptual typology, International Journal of Electronic Commerce, 2002
[Muller & Vercouter, 05] G. Muller and L. Vercouter, Decentralized Monitoring of Agent Communication with a Reputation Model, Trusting Agents for trusting Electronic Societies, LNCS 3577, 2005
[Sabater, 04] Evaluating the ReGreT system Applied Artificial Intelligence ,18 (9-10) :797-813
[Sabater & Sierra, 05] Review on computational trust and reputation models Artificial Intelligence Review ,24 (1) :33-60
[Sabater-Mir & Paolucci, 06] Repage: REPutation and imAGE among limited autonomous partners, JASSS - Journal of Artificial Societies and Social Simulation ,9 (2), 2006
[Schillo & Funk, 99] M. Schillo and P. Funk, Learning from and about other agents in terms of social metaphors, Agents Learning About From and With Other Agents, 1999
