Consensus formation is investigated for multi-agent systems in which agents' beliefs are both vague and uncertain. Vagueness is represented by a third truth state meaning borderline. This is combined with a probabilistic model of uncertainty. A belief combination operator is then proposed, which exploits borderline truth values to enable agents with conflicting beliefs to reach a compromise. A number of simulation experiments are carried out, in which agents apply this operator in pairwise interactions, under the bounded confidence restriction that the two agents' beliefs must be sufficiently consistent with each other before agreement can be reached. As well as studying the consensus operator in isolation, we also investigate scenarios in which agents are influenced either directly or indirectly by the state of the world. For the former, we conduct simulations that combine consensus formation with belief updating based on evidence. For the latter, we investigate the effect of assuming that the closer an agent's beliefs are to the truth the more visible they are in the consensus building process. In all cases, applying the consensus operators results in the population converging to a single shared belief that is both crisp and certain. Furthermore, simulations that combine consensus formation with evidential updating converge more quickly to a shared opinion, which is closer to the actual state of the world than those in which beliefs are only changed as a result of directly receiving new evidence. Finally, if agent interactions are guided by belief quality measured as similarity to the true state of the world, then applying the consensus operator alone results in the population converging to a high-quality shared belief.
One common feature of most of the studies mentioned above is that, either explicitly or implicitly, they interpret the third truth value as meaning uncertain' or unknown'. In contrast, as stated in section 1, we intend the middle truth value to refer to borderline cases resulting from the underlying vagueness of the language. So, for example, giving the proposition Ethel is short' the intermediate truth value means that Ethel's height is borderline short not short, rather than meaning that Ethel's height is unknown. This approach allows us to distinguish between vagueness and uncertainty so that, for instance, based on their knowledge of Ethel's height an agent could be certain that she is borderline short. A more detailed analysis of the difference between these two possible interpretations of the third truth state is given in @cite_20 .
@cite_20
1989393769 1014449310 1986099962 1250241298
In this paper we compare the expressive power of elementary representation formats for vague, incomplete or conflicting information. These include Boolean valuation pairs introduced by Lawry and Gonzalez-Rodriguez, orthopairs of sets of variables, Boolean possibility and necessity measures, three-valued valuations, supervaluations. We make explicit their connections with strong Kleene logic and with Belnap logic of conflicting information. The formal similarities between 3-valued approaches to vagueness and formalisms that handle incomplete information often lead to a confusion between degrees of truth and degrees of uncertainty. Yet there are important differences that appear at the interpretive level: while truth-functional logics of vagueness are accepted by a part of the scientific community (even if questioned by supervaluationists), the truth-functionality assumption of three-valued calculi for handling incomplete information looks questionable, compared to the non-truth-functional approaches based on Boolean possibility-necessity pairs. This paper aims to clarify the similarities and differences between the two situations. We also study to what extent operations for comparing and merging information items in the form of orthopairs can be expressed by means of operations on valuation pairs, three-valued valuations and underlying possibility distributions. We explore the connections between several representations of imperfect information.In each case we compare the expressive power of these formalisms.In each case we study how to express aggregation operations.We demonstrate the formal similarities among these approaches.We point out the differences in interpretations between these approaches. In this paper, we extend existing work on latent attribute inference by leveraging the principle of homophily: we evaluate the inference accuracy gained by augmenting the user features with features derived from the Twitter profiles and postings of her friends. We consider three attributes which have varying degrees of assortativity: gender, age, and political affiliation. Our approach yields a significant and robust increase in accuracy for both age and political affiliation, indicating that our approach boosts performance for attributes with moderate to high assortativity. Furthermore, different neighborhood subsets yielded optimal performance for different attributes, suggesting that different subsamples of the user's neighborhood characterize different aspects of the user herself. Finally, inferences using only the features of a user's neighbors outperformed those based on the user's features alone. This suggests that the neighborhood context alone carries substantial information about the user. Vague information is common in many database applications due to intensive data dissemination arising from different pervasive computing sources, such as the high volume data obtained from sensor networks and mobile communications. In this paper, we utilize functional dependencies (FDs) and inclusion dependencies (INDs), which are the most fundamental integrity constraints that arise in practice in relational databases, to maintain the consistency of a vague database. First, we tackle the problem, given a vague relation r and a set of FDs F, of how to obtain the ''best'' approximation of r with respect to F when taking into account the median membership (m) and the imprecision membership (i) thresholds. Using these two thresholds of a vague set, we define the notion of mi-overlap between vague sets and a merge operation on r. Second, we consider, given a vague database d and a set of INDs N, how to obtain the minimal possible change in value-precision for d. Satisfaction of an FD in r is defined in terms of values being mi-overlapping while satisfaction of an IND in d is defined in terms of value-precision. We show that Lien's and Atzeni's axiom system is sound and complete for FDs being satisfied in vague relations and that 's axiom system is sound and complete for INDs being satisfied in vague databases. Finally, we study the chase procedure VChase(d,[email protected]?N) as a means to maintain consistency of d with respect to F and N. Our main result is that the output of the procedure is the most object-precise approximation of r with respect to F and the minimum value-precision change of d with respect to N. The complexity of VChase(r,F) is polynomial time in the sizes of r and F whereas the complexity of VChase(d,[email protected]?N) is exponential. Uncertainty is an important linguistic phenomenon that is relevant in many fields of language processing. In its most general sense, it can be interpreted as lack of information: the hearer or the reader cannot be certain about some pieces of information. Thus, uncertain propositions are those whose truth value or reliability cannot be determined due to lack of information. Distinguishing between factual (i.e. true or false) and uncertain propositions is of primary importance both in linguistics and natural language processing applications. For instance, in information extraction an uncertain piece of information might be of some interest for an end-user as well, but such information must not be confused with factual textual evidence (reliable information) and the two should be kept separated. The main objective of this thesis is to detect uncertainty in English and Hungarian natural language texts. As opposed to earlier studies that focused on specific domains and were English-oriented, we will offer here a comprehensive approach to uncertainty detection, which can be easily adapted to the specific needs of many domains and languages. In our investigations, we will pay attention to create linguistically plausible models of uncertainty that will be exploited in creating manually annotated corpora that will serve as the base for the implementation of our uncertainty detectors for several domains, with the help of supervised machine learning techniques. Furthermore, we will also demonstrate that uncertainty detection can be fruitfully applied in a real-world application, namely, information extraction from clinical discharge summaries.