A crucial problem in the design of multi-issue group decisions is the definition of rules that select outputs that are consistent with existing correlations between multiple issues. A less known problem arises when the collective outcome is supported by none or by the fewest individuals, bringing into question the compatibility of a collective decision with respect to individual choices. The ai...

This paper studies by means of reciprocal fuzzy binary relations the aggregation of preferences when individuals show their preferences gradually. We have characterized neutral aggregation rules through functions from powers of the unit interval in the unit interval. Furthermore, we have determined the neutral aggregation rules that are decomposable and anonymous. In this class of rules, the co...

We study rank aggregation algorithms that take as input the opinions of players over their peers, represented as rankings, and output a social ordering of the players (which reflects, e.g., relative contribution to a project or fit for a job). To prevent strategic behavior, these algorithms must be impartial, i.e., players should not be able to influence their own position in the output ranking...

Cooperative anchoring is the sharing of associations between symbols and sensor data across multi robot systems. We apply the solution of Judgment Aggregation, a logic based collective-decision making framework from social choice theory to the problem of cooperative anchoring in terms of information fusion. We model a multi-agent system comprising of nao robots on a search exercise using its vi...

Aggregation is a sub-task of Natural Language Generation (NLG) that improves the conciseness and readability of the text outputted by NLG systems. Till date, approaches towards the aggregation task have been predominantly manual (manual analysis of domain specific corpus and development of rules). In this paper, a new algorithm for aggregation in NLG is proposed, that learns context sensitive a...

In the era of Big Data, knowledge integration is key for tasks such as social media aggregation, opinion mining, and cyber-issue detection. The integration of different kinds of knowledge coming from multiple sources, however, is often a problematic issue as it either requires a lot of manual effort in defining aggregation rules or suffers from noise generated by automatic integration technique...

In OpenFlow-driven SDN, flow tables are TCAM-hungry; commodity switches suffer from limited concrete flow table size. One method for coping with the limitations is to use aggregation schemes to reduce the number of flow entries required to express the same forwarding semantics. Unfortunately, the aggregation of rules would retard table updates and lengthen the updating duration, during which, t...

This article provides a novel perspective on the vexed issue of the relation between probability and rational acceptability, exploiting a recently-noted structural parallel with the problem of judgment aggregation. After offering a number of general desiderata on the relation between finite probability models and sets of accepted sentences in a Boolean sentential language, it is noted that a nu...

Abstract Complex reasoning problems are most clearly and easily specified using logical rules, but require recursive rules with aggregation such as count sum for practical applications. Unfortunately, the meaning of has been a significant challenge, leading to many disagreeing semantics. This paper describes unified semantics aggregation, extending founded constraint negation. The key idea is s...