Automated Negotiations Under User Preference Uncertainty: A Linear Programming Approach

Tsimpoukis, Dimitrios; Baarslag, Tim; Kaisers, Michael; Paterakis, Nikolaos G.

doi:10.1007/978-3-030-17294-7_9

Cited by 7 publications

(7 citation statements)

References 20 publications

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“…CP-nets, which provide a qualitative representation of preferences that reflects conditional dependence [19], were also studied in the automated negotiation context [7,78]. Furthermore, Tsimpoukis et al proposed a decision model that uses linear optimization to translate partial information into utility estimates based on a set of ranked outcomes [116]. However, as we explain later in Sect.…”

Section: Privacy Preference Elicitationmentioning

confidence: 99%

“…In a negotiation, a user has a specific set of preferences regarding the possible outcomes. The so-called preference profile is given by an ordinal ranking over the set of the outcomes: an outcome is said to be weakly preferred over an outcome ′ if ⪰ � where , � ∈ or strictly preferred if 𝜔 ≻ 𝜔 ′ [116]. Given the outcome ranking, the agent's goal is to formulate its estimate of the valuation function v p that approximates the real user's valuation as much as possible so that the preferences are expressed in a cardinal way:…”

Section: Preference Elicitationmentioning

confidence: 99%

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Automated privacy negotiations with preference uncertainty

Filipczuk

Baarslag

Gerding

et al. 2022

Auton Agent Multi-Agent Syst

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Many service providers require permissions to access privacy-sensitive data that are not necessary for their core functionality. To support users’ privacy management, we propose a novel agent-based negotiation framework to negotiate privacy permissions between users and service providers using a new multi-issue alternating-offer protocol based on exchanges of partial and complete offers. Additionally, we introduce a novel approach to learning users’ preferences in negotiation and present two variants of this approach: one variant personalised to each individual user, and one personalised depending on the user’s privacy type. To evaluate them, we perform a user study with participants, using an experimental tool installed on the participants’ mobile devices. We compare the take-it-or-leave-it approach, in which users are required to accept all permissions requested by a service, to negotiation, which respects their preferences. Our results show that users share personal data 2.5 times more often when they are able to negotiate while maintaining the same level of decision regret. Moreover, negotiation can be less mentally demanding than the take-it-or-leave-it approach and it allows users to align their privacy choices with their preferences. Finally, our findings provide insight into users’ data sharing strategies to guide the future of automated and negotiable privacy management mechanisms.

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Section: Privacy Preference Elicitationmentioning

confidence: 99%

Section: Preference Elicitationmentioning

confidence: 99%

Automated privacy negotiations with preference uncertainty

Filipczuk

Baarslag

Gerding

et al. 2022

Auton Agent Multi-Agent Syst

Self Cite

View full text Add to dashboard Cite

show abstract

“…Note that in multi-objective settings, agents and/or users are often incapable of specifying their utilities numerically [133]. However, recently there has been research in automated negotiation focusing on preference uncertainty [117], i.e., uncertainty about the individual utility functions, and eliciting preferences [9], making realistic negotiation with the PCS of a multi-objective decision problem as input, possible. Under ESR the situation becomes significantly more complex, i.e., in general, the undominated set is defined as: Definition 7 The undominated set of policies (U) under possibly non-linear monotonically increasing u, under ESR, is the subset of the set of all admissible joint policies Π for which there exists a u for which the scalarised value is maximal:…”

Section: Pareto Coverage Setsmentioning

confidence: 99%

Multi-objective multi-agent decision making: a utility-based analysis and survey

Rădulescu

Mannion

Roijers

et al. 2019

Auton Agent Multi-Agent Syst

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The majority of multi-agent system (MAS) implementations aim to optimise agents' policies with respect to a single objective, despite the fact that many real-world problem domains are inherently multi-objective in nature. Multiobjective multi-agent systems (MOMAS) explicitly consider the possible trade-offs between conflicting objective functions. We argue that, in MOMAS, such compromises should be analysed on the basis of the utility that these compromises have for the users of a system. As is standard in multi-objective optimisation, we model the user utility using utility functions that map value or return vectors to scalar values. This approach naturally leads to two different optimisation criteria: expected scalarised returns (ESR) and scalarised expected returns (SER). We develop a new taxonomy which classifies multi-objective multi-agent decision making settings, on the basis of the reward structures, and which and how utility functions are applied. This allows us to offer a structured view of the field, to clearly delineate the current state-of-the-art in multi-objective multi-agent decision making approaches and to identify promising directions for future research. Starting from the execu-

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“…And we can only get the sum value of x i t + y i t . The DDoS problem cannot be solved by Linear Programming (LP) method [16], [17] to get the optimal router throttling rate a i t for each router. Thus, if we want to make decisions based on incomplete, vague information, we need to get some feedback information from the network environment to tell us whether the decision we just made is efficiency.…”

Section: A Ddos Problem Definitionmentioning

confidence: 99%

A New Smart Router-Throttling Method to Mitigate DDoS Attacks

et al. 2019

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The distributed denial of service (DDoS) attack is one of the most server threats to the current Internet and brings huge losses to society. Furthermore, it is challenging to defend DDoS due to the case that the DDoS traffic can appear similar to the legitimate ones. Router throttling is an accessible approach to defend DDoS attacks. Some existing router throttling methods dynamically adjust a given threshold value to keep the server load safe. However, these methods are not ideal as they exploit the information of the current time, so the perception of time series variations is poor. The DDoS problem can be seen as a Markov decision process (MDP). Multi-agent router throttling (MART) method based on hierarchical communication mechanism has been proposed to address this problem. However, each agent is independent with each other and has no communication among them, therefore, it is hard for them to collaborate to learn an ideal policy to defend DDoS. To solve this multi-agent partially observable MDP problem, we propose a centralized reinforcement learning router throttling method based on a centralized communication mechanism. Each router sends its own traffic reading to a central router, the central router then makes a decision for each router to choose the throttling rate. We also simulate the environment of the DDoS problem more realistic while modify the reward function of the MART to make the reward function of more coherent. To decrease the communication costs, we add a deep deterministic policy gradient network for each router to decide whether or not to send information to the central agent. The experiments validate that our proposed new smart router throttling method outperforms existing methods to the DDoS instruction response.INDEX TERMS Distributed denial of service, router throttling, Markov decision process, multi-agent router throttling, hierarchical communication, centralized communication, communication costs.

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Automated Negotiations Under User Preference Uncertainty: A Linear Programming Approach

Cited by 7 publications

References 20 publications

Automated privacy negotiations with preference uncertainty

Automated privacy negotiations with preference uncertainty

Multi-objective multi-agent decision making: a utility-based analysis and survey

A New Smart Router-Throttling Method to Mitigate DDoS Attacks

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