Marija Slavkovik

Judgment aggregation rules based on minimization

Many voting rules are based on some minimization principle. Likewise, in the field of logic-based knowledge representation and reasoning, many belief change or inconsistency handling operators also make use of minimization. Surprisingly, minimization has not played a major role in the field of judgment aggregation, in spite of its proximity to voting theory and logic-based knowledge representation and reasoning. Here we make a step in this direction and study six judgment aggregation rules; two of them, based on distances, have been previously defined; the other four are new, and all inspired both by voting theory and knowledge representation and reasoning. We study the inclusion relationships between these rules and address some of their social choice theoretic properties.

A Complete Conclusion-Based Procedure for Judgment Aggregation

Judgment aggregation is a formal theory reasoning about how a group of agents can aggregate individual judgments on connected propositions into a collective judgment on the same propositions. Three procedures for successfully aggregating judgments sets are: premise-based procedure, conclusion-based procedure and distance-based merging. The conclusion-based procedure has been little investigated because it provides a way to aggregate the conclusions, but not the premises, thus it outputs an incomplete judgment set. The goal of this paper is to present a conclusion-based procedure outputting complete judgment sets.

Group intention is social choice with commitment

An agent intends g if it has chosen to pursue goal g an is committed to pursuing g . How do groups decide on a common goal? Social epistemology offers two views on collective attitudes: according to the summative approach, a group has attitude P if all or most of the group members have the attitude P; according to the non-summative approach, for a group to have attitude P it is required that the members together agree that they have attitude P. The summative approach is used extensively in multi-agent systems. We propose a formalization of non-summative group intentions, using social choice to determine the group goals. We use judgment aggregation as a decision-making mechanism and a multi-modal multi-agent logic to represent the collective attitudes, as well as the commitment and revision strategies for the groups intentions.

New Directions in Logic, Language and Computation

In this paper we present a continual context-sensitive abductive framework for understanding situated spoken natural dialogue. The framework builds up and refines a set of partial defeasible explanations of the spoken input, trying to infer the speaker’s intention. These partial explanations are conditioned on the eventual verification of the knowledge gaps they contain. This verification is done by executing test actions, thereby going beyond the initial context. The approach is illustrated by an example set in the context of human-robot interaction.

A Satisficing Agreements Model

Satisficing, the concept proposed by Herbert Simon, as an approach to reaching agreements is little explored. We propose a model for satisficing agreement reaching for an adaptive collaborative group of agents. The group consists of one human agent familiar with the problem and arbitrarily many artificial agents. Our model raises to the team level the recognition-primed decision model constructed in the field of cognitive decision-making by using social choice for reaching group opinions.

Recognition-primed group decisions via judgement aggregation

We introduce a conceptual model for reaching group decisions. Our model extends a well-known, single-agent cognitive model, the recognition-primed decision (RPD) model. The RPD model includes a recognition phase and an evaluation phase. Group extensions of the RPD model, applicable to a group of RPD agents, have been considered in the literature, however the proposed models do not formalize how distributed and possibly inconsistent information can be combined in either phase. We show how such information can be utilized by aggregating it using a specific social choice method, namely judgment aggregation. Our model is applicable to hierarchical groups of agents containing at least one RPD agent.