Assessment of trust and reputation typically relies on prior experiences of a trustee agent, which may not exist, e.g. especially in highly dynamic environments. In these cases stereotypes can be used, where traits of trustees can be used as an indicator of their behaviour during interactions. Communicating observations of traits to witnesses who are unable to observe them is difficult, however, when the traits are interpreted subjectively. In this paper we propose a mechanism for learning translations between such subjective observations, evaluating it in a simulated marketplace

Barakat, Lina

Griffiths, Nathan

Miles, Simon

Taylor, Phillip

King's Research Portal

                       King’s Research Portal  Document VersionPeer reviewed versionLink to publication record in King's Research PortalCitation for published version (APA):Taylor, P., Griffiths, N., Barakat, L., & Miles, S. (Accepted/In press). Bootstrapping Trust with Partial andSubjective Observability. In Proceedings of the 16th International Conference on Autonomous Agents andMultiagent Systems (Vol. 3, pp. 1745-1747)Citing this paperPlease note that where the full-text provided on King's Research Portal is the Author Accepted Manuscript or Post-Print version this maydiffer from the final Published version. If citing, it is advised that you check and use the publisher's definitive version for pagination,volume/issue, and date of publication details. And where the final published version is provided on the Research Portal, if citing you areagain advised to check the publisher's website for any subsequent corrections.General rightsCopyright and moral rights for the publications made accessible in the Research Portal are retained by the authors and/or other copyrightowners and it is a condition of accessing publications that users recognize and abide by the legal requirements associated with these rights.•Users may download and print one copy of any publication from the Research Portal for the purpose of private study or research.•You may not further distribute the material or use it for any profit-making activity or commercial gain•You may freely distribute the URL identifying the publication in the Research PortalTake down policyIf you believe that this document breaches copyright please contact librarypure@kcl.ac.uk providing details, and we will remove access tothe work immediately and investigate your claim.Download date: 07. Oct. 2023Bootstrapping Trust with Partial and SubjectiveObservability(Extended Abstract)Phillip Taylor, Nathan GriffithsThe University of WarwickCoventry, CV4 7AL, UK{name.surname}@warwick.ac.ukLina Barakat, Simon MilesKings College LondonLondon, WC2R 2LS, UK{name.surname}@kcl.ac.ukABSTRACTAssessment of trust and reputation typically relies on priorexperiences of a trustee agent, which may not exist, e.g.especially in highly dynamic environments. In these casesstereotypes can be used, where traits of trustees can be usedas an indicator of their behaviour during interactions. Com-municating observations of traits to witnesses who are un-able to observe them is difficult, however, when the traits areinterpreted subjectively. In this paper we propose a mech-anism for learning translations between such subjective ob-servations, evaluating it in a simulated marketplace.CCS Concepts•Computing methodologies → Multi-agent systems;KeywordsTrust and reputation, Stereotypes, Machine learning1. INTRODUCTIONIn multi-agent systems agents can use trust and reputa-tion to decide which others to interact with [3,18,20]. Trustis the degree of belief, from the trustor agent’s perspective,that a trustee agent will act as they say they will in a givencontext [1,2,8]. Whereas trust is assessed using experiencesof the trustor, reputation is based on the opinions of severalagents. In assessing a trustee, the trustor combines:Direct-trust — trustor experiences with the trusteeWitness-reputation — witness reports of the trusteeStereotype-trust — trustor experiences and observed traitsStereotype-reputation — witness reports of observed traitsDirect-trust requires the trustor to have previously inter-acted with the trustee. The same is true for witnesses whencomputing witness-reputation, where the trustor requestsopinions about a specific trustee. In combination, direct-trust and witness-reputation, make up the Beta Reputa-tion System (BRS), as proposed by Jøsang et al. [7]. Otherreputation systems that combine these include FIRE [4, 5],TRAVOS [16], BLADE [13], and HABIT [15].Appears in: Proc. of the 16th International Conference onAutonomous Agents and Multiagent Systems (AAMAS 2017),S. Das, E. Durfee, K. Larson, M. Winikoff (eds.),May 8–12, 2017, São Paulo, Brazil.Copyright c© 2017, International Foundation for Autonomous Agentsand Multiagent Systems (www.ifaamas.org). All rights reserved.Stereotype-trust enables assessments of trustees where di-rect experience is limited, by assuming trustees with sim-ilar traits behave similarly. Similar to witness-reputation,stereotype-reputation is gathered from witnesses. Liu etal. [9, 10] proposed that trustee traits, correlated with theirtrustworthiness, be used to separate them into groups de-fined by their common characteristics. Instead of clusteringtrustees, Burnett et al. [2] use regression models to maptraits to trustworthiness, and use predictions as base valuesin a probabilistic trust model. When stereotype-trust is un-available, because of a lack of data, stereotype-reputationis gathered from witnesses. This model is extended by Şen-soy et al. [14] to discount witness reports, and bootstrap thisdiscounting using stereotypes of witnesses.Existing reputation models require the following:• The trustee is identified and the witness can observe itstraits (i.e. trustees are fully observable), or• All agents observe trustee traits in the same way (i.e.trustee traits are objective).In real-world environments, however, trustees may be par-tially observable and observations may be subjective. Whentrustees are partially observable and the witness is unableto observe the traits, the trustor must disclose their obser-vations for the witness to process them. If a new trustee isunknown to a witness, for example, the trustor must describetheir observations. If trait observations are also subjective,those observed by a trustor may be meaningless to a witness.We define the set of traits in an environment as Θ, whichmay include ‘airport transfer’ and ‘suitcase storage’ for ataxi marketplace. Each trustee agent, te, exhibits a subsetof these traits, θte ⊆ Θ, and each trustor agent, tr, hasan observation function, Otr : P(Θ) → P(Θ), which deter-mines their observations of a trustee, θtetr = Otr(θte).In fully observable settings, witnesses can always observetraits of trustees, θtew = Ow(te), and correctly interpret anyassociated stereotype. With partial observability witnessesmay be unable to observe the traits of a trustee and mustprocess those observed by the trustor, which, if objective,poses no problem. With subjectivity, however, observationsmade by a trustor may be different to those that a witnesswould make. Two customers may have different interpre-tations of suitable suitcase storage for a taxi, for example,leading to misunderstandings when communicating traits.2. POSSTR MODELAn interaction is recorded as 〈tr, te, θtetr , rtetr〉, where θtetr areobservations of te by tr before the interaction, and rtetr is therating given by tr. We assume ratings are binary, with 1indicating a good outcome and 0 indicating otherwise.As with STAGE [14] and Burnett et al. [2], POSSTR isbased on BRS [7], which represents and processes opinionsusing subjective logic [6]. The overall trust value is com-puted as the likelihood of a successful interaction,P (r̂tetr = 1) =p+ 2ap+ n+ 2, (1)where p is the number of previous successful interactions,n is the number of unsuccessful ones, and a is a Bayesianprior. For direct-trust, p and n are calculated using interac-tions recorded by tr. In witness-reputation, the interactionsreported by witnesses are combined when calculating p andn. In BRS [7], a = 0.5 to represent equal likelihood of a suc-cessful and unsuccessful outcome with no information. Withstereotypes [2,14], the value of a is the output of models thatmap trustee traits to trustworthiness, f : P(Θ)→R.For stereotype-trust, ftr is learned by generating a trainingsample for each agent tr has previously interacted with. Theinput features are the observed traits, θtetr , and the target isthe direct-trust that tr has in te, with a prior of a = 0.5. AnM5 model tree [11] is then learned to map traits observedby tr to the trust in agents that express those traits. M5recursively splits training samples using the values of thefeatures that best discriminate the target. Whereas in typ-ical decision trees the leaves are target values, leaves in M5are regression models that output the target value, which istaken as the stereotype-trust prior, a = atr = ftr(θtetr).When assessing stereotype-reputation, a witness may nothave observed the traits themselves, meaning a translation,ftr→w : P(Θ) → P(Θ), is required and learned from train-ing data generated from common observations that boththe witness and the trustor have made. When requestinga stereotype assessment from a witness, either the trustorprovides their observations of other trustee agents to thewitness or vice versa. These observations, consist of the ob-served traits along with the trustee identifier. As an ex-ample, consider that the trustor has observed the traitsof three trustees, {θte1tr , θte2tr , θte3tr }, and a witness has ob-served those of two, {θte1w , θte2w }. Training data can thenbe generated by matching up the common observations, as{θte1tr : θte1w , θte2tr : θte2w }, where ‘:’ separates the inputs andoutputs. These observations may have been made withouthaving interacted with the trustees, such as during a repu-tation assessment without using the service. These commonobservations samples form the training data that can be in-put into a multi-target learning algorithm [12].In this paper the binary relevance method [17] is used,and Naive Bayes [12, 19] models are learned to map traitsobserved by the trustor to each trait that would be observedby the witness. The traits observed by the trustor are theninput into each of the learned models and their outputs arecombined to be the traits the witness would have observed.The Bayesian prior for stereotype-reputation is then com-puted as the mean over the trustor and witnesses,a =1|W |+ 1(atr +∑w∈Waw), (2)and is used in Equation 1, where,aw ={fw(θtew ) if witness observed trustee,fw(ftr→w(θtetr)) otherwise.(3)Full Limited None0100200Observability typeUtilityRandomTT+RT+STT+R+STT+R+STRPOSSTRFigure 1: Utility under different observabilities.3. RESULTSPOSSTR was evaluated against combinations of direct-trust (T), witness-reputation (R), stereotype-trust (ST) andstereotype-reputation (STR) using a simulated marketplaceover 250 rounds, consisting 100 trustee agents and 20 trustoragents. In each round, each trustor selected from 10 randomtrustees using reputation gathered from a random 10 wit-nesses (other trustor agents). The trustee with highest rep-utation was selected for an interaction, where the outcomeutility was drawn from a normal distribution assigned to thetrustee at the beginning. If the outcome utility was greaterthan 0.5, the interaction was rated as a success and other-wise it was rated as unsuccessful. All trustor and trusteeagents left the simulation in each with a probability of 0.05,to be replaced by another.Figure 1 shows the total utility gained by trustors underdifferent levels of observability. The results are averagedover 50 simulations and the error bars represent the stan-dard deviation. In general, using both direct-trust alongsidewitness-reputation gained higher utilities than using direct-trust only. Similarly, using stereotypes led to higher utilitiesthan using direct-trust and witness-reputation only. In fullobservability, witnesses were always able to observe traitsthemselves when reporting for stereotype-reputation, lead-ing to no benefit in the translation function used by POS-STR over TR+STR. With limited observability, where wit-nesses were able only to recall traits observed during theirown previous assessments (as a trustor), the TR+STR strat-egy gained less utility and was outperformed by POSSTR.To limit the observability further we had the trustor con-ceal the identity of the trustee being assessed from wit-nesses, meaning that witness-reputation was not possibleand stereotype-reputation assessments relied on the traitsobserved subjectively by the trustor. Here, TR+STR gainedmuch less utility than with more observability, whereas POS-STR was again unaffected and performed the best.4. CONCLUSIONIn this paper we have presented the POSSTR reputationsystem, that is robust in subjective and partially observ-able environments. We found that in environments with fullobservability or with objectively observable traits, POSSTRperformed equally well as TR+STR. With decreased observ-ability and with subjective traits, the translation function inPOSSTR allowed it to make reliable reputation assessments.REFERENCES[1] D. Artz and Y. Gil. A survey of trust in computerscience and the semantic web. Web Semantics:Science, Services and Agents on the World Wide Web,5(2):58–71, June 2007.[2] C. Burnett, T. Norman, and K. Sycara. Stereotypicaltrust and bias in dynamic multiagent systems. ACMTransactions on Intelligent Systems and Technology,4(2):26, 2013.[3] F. Hendrikx, K. Bubendorfer, and R. Chard.Reputation systems: A survey and taxonomy. Journalof Parallel and Distributed Computing, 75:184 – 197,2015.[4] T. D. Huynh, N. R. Jennings, and N. R. Shadbolt.FIRE: An integrated trust and reputation model foropen multi-agent systems. In 16th EuropeanConference on Artificial Intelligence, pages 18–22,2004.[5] T. D. Huynh, N. R. Jennings, and N. R. Shadbolt. Anintegrated trust and reputation model for openmulti-agent systems. Autonomous Agents andMulti-Agent Systems, 13(2):119–154, 2006.[6] A. Jøsang. A logic for uncertain probabilities.International Journal of Uncertainty, Fuzziness andKnowledge-Based Systems, 09(03):279–311, 2001.[7] A. Jøsang and R. Ismail. The Beta reputation system.In Proceedings of the 15th Bled Electronic CommerceConference, pages 41–55, 2002.[8] A. Jøsang, R. Ismail, and C. Boyd. A survey of trustand reputation systems for online service provision.Decision support systems, 43(2):618–644, 2007.[9] X. Liu, A. Datta, and K. Rzadca. Trust beyondreputation: A computational trust model based onstereotypes. Electronic Commerce Research andApplications, 12:24–39, 2013.[10] X. Liu, A. Datta, K. Rzadca, and E.-P. Lim.Stereotrust: A group based personalized trust model.In Proceedings of the 18th ACM Conference onInformation and Knowledge Management, CIKM ’09,pages 7–16, New York, NY, USA, 2009. ACM.[11] R. J. Quinlan. Learning with continuous classes. In 5thAustralian Joint Conference on Artificial Intelligence,pages 343–348, Singapore, 1992. World Scientific.[12] J. Read, P. Reutemann, B. Pfahringer, andG. Holmes. Meka: A multi-label/multi-targetextension to weka. Journal of Machine LearningResearch, 17(21):1–5, 2016.[13] K. Regan, P. Poupart, and R. Cohen. Bayesianreputation modeling in e-marketplaces sensitive tosubjectivity, deception and change. In Proceedings ofthe National Conference on Artificial Intelligence,pages 1206–1212, 2006.[14] M. Şensoy, B. Yilmaz, and T. J. Norman. STAGE:Stereotypical trust assessment through graphextraction. Computational Intelligence, 32(1):72–101,2016.[15] W. L. Teacy, M. Luck, A. Rogers, and N. R. Jennings.An efficient and versatile approach to trust andreputation using hierarchical Bayesian modelling.Artificial Intelligence, 193:149–185, 2012.[16] W. L. Teacy, J. Patel, N. R. Jennings, and M. Luck.Coping with inaccurate reputation sources:Experimental analysis of a probabilistic trust model.In International Joint Conference on AutonomousAgents and Multiagent Systems, pages 997–1004, 2005.[17] G. Tsoumakas and I. Katakis. Multi-labelclassification: An overview. International Journal ofData Warehousing and Mining, 3(3):1, 2007.[18] O. Wahab, J. Bentahar, H. Otrok, and A. Mourad. Asurvey on trust and reputation models for webservices: Single, composite, and communities. DecisionSupport Systems, 74:121 – 134, 2015.[19] I. Witten, E. Frank, and M. Hall. Data Mining:Practical Machine Learning Tools and Techniques.Morgan Kaufmann, Jan 2011.[20] H. Yu, Z. Shen, C. Leung, C. Miao, and V. Lesser. Asurvey of multi-agent trust management systems.IEEE Access, 1:35–50, April 2013.

English

Bootstrapping Trust with Partial and Subjective Observability

University of Essex Research Repository

                       King’s Research Portal  Document VersionPeer reviewed versionLink to publication record in King's Research PortalCitation for published version (APA):Taylor, P., Griffiths, N., Barakat, L., & Miles, S. (Accepted/In press). Bootstrapping Trust with Partial andSubjective Observability. In Proceedings of the 16th International Conference on Autonomous Agents andMultiagent Systems (Vol. 3, pp. 1745-1747)Citing this paperPlease note that where the full-text provided on King's Research Portal is the Author Accepted Manuscript or Post-Print version this maydiffer from the final Published version. If citing, it is advised that you check and use the publisher's definitive version for pagination,volume/issue, and date of publication details. And where the final published version is provided on the Research Portal, if citing you areagain advised to check the publisher's website for any subsequent corrections.General rightsCopyright and moral rights for the publications made accessible in the Research Portal are retained by the authors and/or other copyrightowners and it is a condition of accessing publications that users recognize and abide by the legal requirements associated with these rights.•Users may download and print one copy of any publication from the Research Portal for the purpose of private study or research.•You may not further distribute the material or use it for any profit-making activity or commercial gain•You may freely distribute the URL identifying the publication in the Research PortalTake down policyIf you believe that this document breaches copyright please contact librarypure@kcl.ac.uk providing details, and we will remove access tothe work immediately and investigate your claim.Download date: 05. Nov. 2018Bootstrapping Trust with Partial and SubjectiveObservability(Extended Abstract)Phillip Taylor, Nathan GriffithsThe University of WarwickCoventry, CV4 7AL, UK{name.surname}@warwick.ac.ukLina Barakat, Simon MilesKings College LondonLondon, WC2R 2LS, UK{name.surname}@kcl.ac.ukABSTRACTAssessment of trust and reputation typically relies on priorexperiences of a trustee agent, which may not exist, e.g.especially in highly dynamic environments. In these casesstereotypes can be used, where traits of trustees can be usedas an indicator of their behaviour during interactions. Com-municating observations of traits to witnesses who are un-able to observe them is difficult, however, when the traits areinterpreted subjectively. In this paper we propose a mech-anism for learning translations between such subjective ob-servations, evaluating it in a simulated marketplace.CCS Concepts•Computing methodologies → Multi-agent systems;KeywordsTrust and reputation, Stereotypes, Machine learning1. INTRODUCTIONIn multi-agent systems agents can use trust and reputa-tion to decide which others to interact with [3,18,20]. Trustis the degree of belief, from the trustor agent’s perspective,that a trustee agent will act as they say they will in a givencontext [1,2,8]. Whereas trust is assessed using experiencesof the trustor, reputation is based on the opinions of severalagents. In assessing a trustee, the trustor combines:Direct-trust — trustor experiences with the trusteeWitness-reputation — witness reports of the trusteeStereotype-trust — trustor experiences and observed traitsStereotype-reputation — witness reports of observed traitsDirect-trust requires the trustor to have previously inter-acted with the trustee. The same is true for witnesses whencomputing witness-reputation, where the trustor requestsopinions about a specific trustee. In combination, direct-trust and witness-reputation, make up the Beta Reputa-tion System (BRS), as proposed by Jøsang et al. [7]. Otherreputation systems that combine these include FIRE [4, 5],TRAVOS [16], BLADE [13], and HABIT [15].Appears in: Proc. of the 16th International Conference onAutonomous Agents and Multiagent Systems (AAMAS 2017),S. Das, E. Durfee, K. Larson, M. Winikoff (eds.),May 8–12, 2017, Sa˜o Paulo, Brazil.Copyright c© 2017, International Foundation for Autonomous Agentsand Multiagent Systems (www.ifaamas.org). All rights reserved.Stereotype-trust enables assessments of trustees where di-rect experience is limited, by assuming trustees with sim-ilar traits behave similarly. Similar to witness-reputation,stereotype-reputation is gathered from witnesses. Liu etal. [9, 10] proposed that trustee traits, correlated with theirtrustworthiness, be used to separate them into groups de-fined by their common characteristics. Instead of clusteringtrustees, Burnett et al. [2] use regression models to maptraits to trustworthiness, and use predictions as base valuesin a probabilistic trust model. When stereotype-trust is un-available, because of a lack of data, stereotype-reputationis gathered from witnesses. This model is extended by S¸en-soy et al. [14] to discount witness reports, and bootstrap thisdiscounting using stereotypes of witnesses.Existing reputation models require the following:• The trustee is identified and the witness can observe itstraits (i.e. trustees are fully observable), or• All agents observe trustee traits in the same way (i.e.trustee traits are objective).In real-world environments, however, trustees may be par-tially observable and observations may be subjective. Whentrustees are partially observable and the witness is unableto observe the traits, the trustor must disclose their obser-vations for the witness to process them. If a new trustee isunknown to a witness, for example, the trustor must describetheir observations. If trait observations are also subjective,those observed by a trustor may be meaningless to a witness.We define the set of traits in an environment as Θ, whichmay include ‘airport transfer’ and ‘suitcase storage’ for ataxi marketplace. Each trustee agent, te, exhibits a subsetof these traits, θte ⊆ Θ, and each trustor agent, tr, hasan observation function, Otr : P(Θ) → P(Θ), which deter-mines their observations of a trustee, θtetr = Otr(θte).In fully observable settings, witnesses can always observetraits of trustees, θtew = Ow(te), and correctly interpret anyassociated stereotype. With partial observability witnessesmay be unable to observe the traits of a trustee and mustprocess those observed by the trustor, which, if objective,poses no problem. With subjectivity, however, observationsmade by a trustor may be different to those that a witnesswould make. Two customers may have different interpre-tations of suitable suitcase storage for a taxi, for example,leading to misunderstandings when communicating traits.2. POSSTR MODELAn interaction is recorded as 〈tr, te, θtetr , rtetr〉, where θtetr areobservations of te by tr before the interaction, and rtetr is therating given by tr. We assume ratings are binary, with 1indicating a good outcome and 0 indicating otherwise.As with STAGE [14] and Burnett et al. [2], POSSTR isbased on BRS [7], which represents and processes opinionsusing subjective logic [6]. The overall trust value is com-puted as the likelihood of a successful interaction,P (rˆtetr = 1) =p+ 2ap+ n+ 2, (1)where p is the number of previous successful interactions,n is the number of unsuccessful ones, and a is a Bayesianprior. For direct-trust, p and n are calculated using interac-tions recorded by tr. In witness-reputation, the interactionsreported by witnesses are combined when calculating p andn. In BRS [7], a = 0.5 to represent equal likelihood of a suc-cessful and unsuccessful outcome with no information. Withstereotypes [2,14], the value of a is the output of models thatmap trustee traits to trustworthiness, f : P(Θ)→R.For stereotype-trust, ftr is learned by generating a trainingsample for each agent tr has previously interacted with. Theinput features are the observed traits, θtetr , and the target isthe direct-trust that tr has in te, with a prior of a = 0.5. AnM5 model tree [11] is then learned to map traits observedby tr to the trust in agents that express those traits. M5recursively splits training samples using the values of thefeatures that best discriminate the target. Whereas in typ-ical decision trees the leaves are target values, leaves in M5are regression models that output the target value, which istaken as the stereotype-trust prior, a = atr = ftr(θtetr).When assessing stereotype-reputation, a witness may nothave observed the traits themselves, meaning a translation,ftr→w : P(Θ) → P(Θ), is required and learned from train-ing data generated from common observations that boththe witness and the trustor have made. When requestinga stereotype assessment from a witness, either the trustorprovides their observations of other trustee agents to thewitness or vice versa. These observations, consist of the ob-served traits along with the trustee identifier. As an ex-ample, consider that the trustor has observed the traitsof three trustees, {θte1tr , θte2tr , θte3tr }, and a witness has ob-served those of two, {θte1w , θte2w }. Training data can thenbe generated by matching up the common observations, as{θte1tr : θte1w , θte2tr : θte2w }, where ‘:’ separates the inputs andoutputs. These observations may have been made withouthaving interacted with the trustees, such as during a repu-tation assessment without using the service. These commonobservations samples form the training data that can be in-put into a multi-target learning algorithm [12].In this paper the binary relevance method [17] is used,and Naive Bayes [12, 19] models are learned to map traitsobserved by the trustor to each trait that would be observedby the witness. The traits observed by the trustor are theninput into each of the learned models and their outputs arecombined to be the traits the witness would have observed.The Bayesian prior for stereotype-reputation is then com-puted as the mean over the trustor and witnesses,a =1|W |+ 1(atr +∑w∈Waw), (2)and is used in Equation 1, where,aw ={fw(θtew ) if witness observed trustee,fw(ftr→w(θtetr)) otherwise.(3)Full Limited None0100200Observability typeUtilityRandomTT+RT+STT+R+STT+R+STRPOSSTRFigure 1: Utility under different observabilities.3. RESULTSPOSSTR was evaluated against combinations of direct-trust (T), witness-reputation (R), stereotype-trust (ST) andstereotype-reputation (STR) using a simulated marketplaceover 250 rounds, consisting 100 trustee agents and 20 trustoragents. In each round, each trustor selected from 10 randomtrustees using reputation gathered from a random 10 wit-nesses (other trustor agents). The trustee with highest rep-utation was selected for an interaction, where the outcomeutility was drawn from a normal distribution assigned to thetrustee at the beginning. If the outcome utility was greaterthan 0.5, the interaction was rated as a success and other-wise it was rated as unsuccessful. All trustor and trusteeagents left the simulation in each with a probability of 0.05,to be replaced by another.Figure 1 shows the total utility gained by trustors underdifferent levels of observability. The results are averagedover 50 simulations and the error bars represent the stan-dard deviation. In general, using both direct-trust alongsidewitness-reputation gained higher utilities than using direct-trust only. Similarly, using stereotypes led to higher utilitiesthan using direct-trust and witness-reputation only. In fullobservability, witnesses were always able to observe traitsthemselves when reporting for stereotype-reputation, lead-ing to no benefit in the translation function used by POS-STR over TR+STR. With limited observability, where wit-nesses were able only to recall traits observed during theirown previous assessments (as a trustor), the TR+STR strat-egy gained less utility and was outperformed by POSSTR.To limit the observability further we had the trustor con-ceal the identity of the trustee being assessed from wit-nesses, meaning that witness-reputation was not possibleand stereotype-reputation assessments relied on the traitsobserved subjectively by the trustor. Here, TR+STR gainedmuch less utility than with more observability, whereas POS-STR was again unaffected and performed the best.4. CONCLUSIONIn this paper we have presented the POSSTR reputationsystem, that is robust in subjective and partially observ-able environments. We found that in environments with fullobservability or with objectively observable traits, POSSTRperformed equally well as TR+STR. With decreased observ-ability and with subjective traits, the translation function inPOSSTR allowed it to make reliable reputation assessments.REFERENCES[1] D. Artz and Y. Gil. A survey of trust in computerscience and the semantic web. Web Semantics:Science, Services and Agents on the World Wide Web,5(2):58–71, June 2007.[2] C. Burnett, T. Norman, and K. Sycara. Stereotypicaltrust and bias in dynamic multiagent systems. ACMTransactions on Intelligent Systems and Technology,4(2):26, 2013.[3] F. Hendrikx, K. Bubendorfer, and R. Chard.Reputation systems: A survey and taxonomy. Journalof Parallel and Distributed Computing, 75:184 – 197,2015.[4] T. D. Huynh, N. R. Jennings, and N. R. Shadbolt.FIRE: An integrated trust and reputation model foropen multi-agent systems. In 16th EuropeanConference on Artificial Intelligence, pages 18–22,2004.[5] T. D. Huynh, N. R. Jennings, and N. R. Shadbolt. Anintegrated trust and reputation model for openmulti-agent systems. Autonomous Agents andMulti-Agent Systems, 13(2):119–154, 2006.[6] A. Jøsang. A logic for uncertain probabilities.International Journal of Uncertainty, Fuzziness andKnowledge-Based Systems, 09(03):279–311, 2001.[7] A. Jøsang and R. Ismail. The Beta reputation system.In Proceedings of the 15th Bled Electronic CommerceConference, pages 41–55, 2002.[8] A. Jøsang, R. Ismail, and C. Boyd. A survey of trustand reputation systems for online service provision.Decision support systems, 43(2):618–644, 2007.[9] X. Liu, A. Datta, and K. Rzadca. Trust beyondreputation: A computational trust model based onstereotypes. Electronic Commerce Research andApplications, 12:24–39, 2013.[10] X. Liu, A. Datta, K. Rzadca, and E.-P. Lim.Stereotrust: A group based personalized trust model.In Proceedings of the 18th ACM Conference onInformation and Knowledge Management, CIKM ’09,pages 7–16, New York, NY, USA, 2009. ACM.[11] R. J. Quinlan. Learning with continuous classes. In 5thAustralian Joint Conference on Artificial Intelligence,pages 343–348, Singapore, 1992. World Scientific.[12] J. Read, P. Reutemann, B. Pfahringer, andG. Holmes. Meka: A multi-label/multi-targetextension to weka. Journal of Machine LearningResearch, 17(21):1–5, 2016.[13] K. Regan, P. Poupart, and R. Cohen. Bayesianreputation modeling in e-marketplaces sensitive tosubjectivity, deception and change. In Proceedings ofthe National Conference on Artificial Intelligence,pages 1206–1212, 2006.[14] M. S¸ensoy, B. Yilmaz, and T. J. Norman. STAGE:Stereotypical trust assessment through graphextraction. Computational Intelligence, 32(1):72–101,2016.[15] W. L. Teacy, M. Luck, A. Rogers, and N. R. Jennings.An efficient and versatile approach to trust andreputation using hierarchical Bayesian modelling.Artificial Intelligence, 193:149–185, 2012.[16] W. L. Teacy, J. Patel, N. R. Jennings, and M. Luck.Coping with inaccurate reputation sources:Experimental analysis of a probabilistic trust model.In International Joint Conference on AutonomousAgents and Multiagent Systems, pages 997–1004, 2005.[17] G. Tsoumakas and I. Katakis. Multi-labelclassification: An overview. International Journal ofData Warehousing and Mining, 3(3):1, 2007.[18] O. Wahab, J. Bentahar, H. Otrok, and A. Mourad. Asurvey on trust and reputation models for webservices: Single, composite, and communities. DecisionSupport Systems, 74:121 – 134, 2015.[19] I. Witten, E. Frank, and M. Hall. Data Mining:Practical Machine Learning Tools and Techniques.Morgan Kaufmann, Jan 2011.[20] H. Yu, Z. Shen, C. Leung, C. Miao, and V. Lesser. Asurvey of multi-agent trust management systems.IEEE Access, 1:35–50, April 2013.

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Bootstrapping Trust with Partial and Subjective Observability

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