Strategies for avoiding preference profiling in agent-based e-commerce environments

Serrano, Emilio; Such, José M.; Botía, Juan A.; García-Fornes, Ana

doi:10.1007/s10489-013-0448-2

Cited by 21 publications

(18 citation statements)

References 44 publications

(62 reference statements)

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“…The information distribution of a MAP task stresses the issue of privacy, which is one of the basic aspects that must be considered in multi-agent applications [Serrano et al 2013]. Agents manage information that is not relevant for their counterparts or sensitive data of their internal operational mechanisms that they are not willing to disclose.…”

Section: Characterization Of a Map Taskmentioning

confidence: 99%

Cooperative Multi-Agent Planning

et al. 2017

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Cooperative multi-agent planning (MAP) is a relatively recent research field that combines technologies, algorithms and techniques developed by the Artificial Intelligence Planning and Multi-Agent Systems communities. While planning has been generally treated as a single-agent task, MAP generalizes this concept by considering multiple intelligent agents that work cooperatively to develop a course of action that satisfies the goals of the group.This paper reviews the most relevant approaches to MAP, putting the focus on the solvers that took part in the 2015 Competition of Distributed and Multi-Agent Planning, and classifies them according to their key features and relative performance.

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Section: Characterization Of a Map Taskmentioning

confidence: 99%

Cooperative Multi-Agent Planning

et al. 2017

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“…We assume that seller agents follow an approach to build buyer agents' profiles similar to Serrano et al (2013). Based on this, a seller agent marks goods that are not accepted by a buyer agent over the course of a purchase (or transaction) as a negative instance (class "-"), while a seller agent marks goods that buyer agent accepts to buy as a positive instance (class "+").…”

Section: Privacy Lossmentioning

confidence: 99%

“…Buyer agents act on behalf of the users that are interested in acquiring wines. Agents in the e-marketplace follow the negotiation protocol depicted in Figure 1 (Serrano et al, 2013). A buyer agent makes a request to purchase a bottle of wine with a request message.…”

Section: Application Scenariomentioning

confidence: 99%

Automated buyer profiling control based on human privacy attitudes

Such

García-Fornes

Botti

2013

Electronic Commerce Research and Applications

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In e-commerce applications, vendors can construct detailed profiles about customers' preferences, which is known as buyer profiling. These profiles can then be used by vendors in order to perform practices such as price discrimination, poor judgment, etc. The use of pseudonyms and, specially, changing pseudonyms from time to time are known to minimise profiling, minimising the capacity of vendors to perform such practices in turn. Although there are some frameworks and tools that support pseudonym change, there are few proposals that suggest or directly change the pseudonym in an automated fashion. Instead, users are usually provided with the mechanisms to change pseudonyms but without any advise on when they should actually use these mechanisms. In this paper, we present an approach to control buyer profiling by means of automated pseudonym changes performed according to human privacy attitudes. We also present an application scenario and an evaluation of our proposal.

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“…First, the spatial and/or functional distribution of planning agents gives rise to specialized agents that have different knowledge and capabilities. In turn, this information distribution stresses the issue of privacy, which is one of the basic aspects that should be considered in multi-agent applications [33].…”

Section: Introductionmentioning

confidence: 99%

FMAP: Distributed cooperative multi-agent planning

2014

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This paper proposes FMAP (Forward MultiAgent Planning), a fully-distributed multi-agent planning method that integrates planning and coordination. Although FMAP is specifically aimed at solving problems that require cooperation among agents, the flexibility of the domain-independent planning model allows FMAP to tackle multi-agent planning tasks of any type. In FMAP, agents jointly explore the plan space by building up refinement plans through a complete and flexible forward-chaining partial-order planner. The search is guided by h DT G , a novel heuristic function that is based on the concepts of Domain Transition Graph and frontier state and is optimized to evaluate plans in distributed environments. Agents in FMAP apply an advanced privacy model that allows them to adequately keep private information while communicating only the data of the refinement plans that is relevant to each of the participating agents. Experimental results show that FMAP is a general-purpose approach that efficiently solves tightly-coupled domains that have specialized agents and cooperative goals as well as loosely-coupled problems. Specifically, the empirical evaluation shows that FMAP outperforms current MAP systems at solving complex planning tasks that are adapted from the International Planning Competition benchmarks.

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Strategies for avoiding preference profiling in agent-based e-commerce environments

Cited by 21 publications

References 44 publications

Cooperative Multi-Agent Planning

Cooperative Multi-Agent Planning

Automated buyer profiling control based on human privacy attitudes

FMAP: Distributed cooperative multi-agent planning

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