1,384 research outputs found
Manipulation in group argument evaluation.
Given an argumentation framework and a group of agents, the individuals may have divergent opinions on the status of the arguments. If the group needs to reach a common po- sition on the argumentation framework, the question is how the individual evaluations can be mapped into a collective one. This problem has been recently investigated in [1]. In this paper, we study under which conditions these operators are Pareto optimal and whether they are manipulable.Collective decision making; Argumentation; Judgment aggregation; Social choice theory;
Manipulation in Group Argument Evaluation.
Given an argumentation framework and a group of agents, the individuals may have divergent opinions on the status of the arguments. If the group needsto reach a common position on the argumentation framework, the question is how the individual evaluations can be mapped into a collective one. Thisproblem has been recently investigated by Caminada and Pigozzi. In this paper, we investigate the behaviour of two of such operators from a socialchoice-theoretic point of view. In particular, we study under which conditions these operators are Pareto optimal and whether they are manipulable.Social choice theory; Judgment aggregation; Argumentation; Collective decision making;
Process mining techniques applied in industry
Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsGiven the overview of today’s information era, several scientific fields related to data raised. Process Mining is relatively new and it aims to leverage merged techniques from two separate scientific areas: Business Process Management and Data Science. The main purpose of Process Mining is the discovery, monitoring and improvement of real processes. As a result, in the last few years, Process Mining has increased remarkably, and the importance of the process insights has become more and more relevant, directly proportional to the amount and quality of data that supports the analyses.
As a Data Engineer Intern at Nokia, I had the opportunity to be involved in the development phase of two business cases, being part of a team that has the main objective of exploring and analyzing several business processes within the company leveraging Data Science techniques
Letter from the Conference Organizer
Pier Pigozzi writes to introduce the Spring 2013 conference, New Trends in Latin American Constitutionalism
Impunity for Snake Oil Merchants?: The Seventh Circuit Upholds the Class Action as a Vehicle for Consumer Protection
The class action is often the only way for victims of consumer fraud to pursue a remedy. Several federal circuit courts have recently adopted the heightened ascertainability requirement—a requirement that makes certifying a consumer class almost impossible. A plaintiff can only meet the heightened ascertainability requirement by showing that members of her proposed class can be identified in a reliable and administratively feasible way. This typically requires documentary proof of class membership. For classes made up of purchasers of deceptive low-cost products who have not kept their receipts, heightened ascertainability has served as an insurmountable barrier to certification.
In Mullins v. Direct Digital LLC, the Seventh Circuit rejected the adoption of the heighted ascertainability requirement. The court held that nothing in Rule 23 mentioned or implied the requirement, and that Rule 23 and the court’s settled class certification analysis already adequately addressed the policy concerns motivating its adoption. In so holding, the court recognized the negative implications that heightened ascertainability would have on the consumer class action.
The Seventh Circuit got it right in rejecting heightened ascertainability. This rule should be abandoned because it undercuts the core policy behind the class action: the vindication of the rights of a group of people who individually would be without effective strength to bring a corporate defendant to court at all. The Judicial Conference’s Committee on Rules of Practice and Procedure should amend Rule 23 to codify the Seventh Circuit’s approach to class certification outlined in Mullins. Such an amendment would eliminate judicially created hurdles to class certification and preserve the class action as an instrument for consumer protection and deterrent against corporate wrongdoing
Impunity for Snake Oil Merchants?: The Seventh Circuit Upholds the Class Action as a Vehicle for Consumer Protection
The class action is often the only way for victims of consumer fraud to pursue a remedy. Several federal circuit courts have recently adopted the heightened ascertainability requirement—a requirement that makes certifying a consumer class almost impossible. A plaintiff can only meet the heightened ascertainability requirement by showing that members of her proposed class can be identified in a reliable and administratively feasible way. This typically requires documentary proof of class membership. For classes made up of purchasers of deceptive low-cost products who have not kept their receipts, heightened ascertainability has served as an insurmountable barrier to certification.
In Mullins v. Direct Digital LLC, the Seventh Circuit rejected the adoption of the heighted ascertainability requirement. The court held that nothing in Rule 23 mentioned or implied the requirement, and that Rule 23 and the court’s settled class certification analysis already adequately addressed the policy concerns motivating its adoption. In so holding, the court recognized the negative implications that heightened ascertainability would have on the consumer class action.
The Seventh Circuit got it right in rejecting heightened ascertainability. This rule should be abandoned because it undercuts the core policy behind the class action: the vindication of the rights of a group of people who individually would be without effective strength to bring a corporate defendant to court at all. The Judicial Conference’s Committee on Rules of Practice and Procedure should amend Rule 23 to codify the Seventh Circuit’s approach to class certification outlined in Mullins. Such an amendment would eliminate judicially created hurdles to class certification and preserve the class action as an instrument for consumer protection and deterrent against corporate wrongdoing
Harnessing the Power of Collective Intelligence: the Case Study of Voxel-based Soft Robots
The field of Evolutionary Robotics (ER) is concerned with the evolution of artificial agents---robots. Albeit groundbreaking, progress in the field has recently stagnated. In the research community, there is a strong feeling that a paradigm change has become necessary to disentangle ER. In particular, a solution has emerged from ideas from Collective Intelligence (CI). In CI---which has many relevant examples in nature---behavior emerges from the interaction between several components. In the absence of central intelligence, collective systems are usually more adaptable.
In this thesis, we set out to harness the power of CI, focusing on the case study of simulated Voxel-based Soft Robots (VSRs): they are aggregations of homogeneous and soft cubic blocks that actuate by altering their volume. We investigate two axes. First, the morphologies of VSRs are intrinsically modular and an ideal substrate for CI; nevertheless, controllers employed until now do not take advantage of such modularity. Our results prove that VSRs can truly be controlled by the CI of their modules. Second, we investigate the spatial and time scales of CI. In particular, we evolve a robot to detect its global body properties given only local information processing, and, in a different study, generalize better to unseen environmental conditions through Hebbian learning. We also consider how evolution and learning interact in VSRs. Looking beyond VSRs, we propose a novel soft robot formalism that more closely resembles natural tissues and blends local with global actuation.The field of Evolutionary Robotics (ER) is concerned with the evolution of artificial agents---robots. Albeit groundbreaking, progress in the field has recently stagnated. In the research community, there is a strong feeling that a paradigm change has become necessary to disentangle ER. In particular, a solution has emerged from ideas from Collective Intelligence (CI). In CI---which has many relevant examples in nature---behavior emerges from the interaction between several components. In the absence of central intelligence, collective systems are usually more adaptable.
In this thesis, we set out to harness the power of CI, focusing on the case study of simulated Voxel-based Soft Robots (VSRs): they are aggregations of homogeneous and soft cubic blocks that actuate by altering their volume. We investigate two axes. First, the morphologies of VSRs are intrinsically modular and an ideal substrate for CI; nevertheless, controllers employed until now do not take advantage of such modularity. Our results prove that VSRs can truly be controlled by the CI of their modules. Second, we investigate the spatial and time scales of CI. In particular, we evolve a robot to detect its global body properties given only local information processing, and, in a different study, generalize better to unseen environmental conditions through Hebbian learning. We also consider how evolution and learning interact in VSRs. Looking beyond VSRs, we propose a novel soft robot formalism that more closely resembles natural tissues and blends local with global actuation
Letter from the Conference Organizer
Pier Pigozzi writes to introduce the Spring 2013 conference, New Trends in Latin American Constitutionalism
Pareto Optimality and Strategy Proofness in Group Argument Evaluation (Extended Version)
An inconsistent knowledge base can be abstracted as a set of arguments and a
defeat relation among them. There can be more than one consistent way to
evaluate such an argumentation graph. Collective argument evaluation is the
problem of aggregating the opinions of multiple agents on how a given set of
arguments should be evaluated. It is crucial not only to ensure that the
outcome is logically consistent, but also satisfies measures of social
optimality and immunity to strategic manipulation. This is because agents have
their individual preferences about what the outcome ought to be. In the current
paper, we analyze three previously introduced argument-based aggregation
operators with respect to Pareto optimality and strategy proofness under
different general classes of agent preferences. We highlight fundamental
trade-offs between strategic manipulability and social optimality on one hand,
and classical logical criteria on the other. Our results motivate further
investigation into the relationship between social choice and argumentation
theory. The results are also relevant for choosing an appropriate aggregation
operator given the criteria that are considered more important, as well as the
nature of agents' preferences
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