Web Intelligence

In this paper, we present an approach based on the principle that psychological capacities, especially personality traits, influence the decision making process of rational agents. Using a three-level (trait, facet, scheme) extension of the FFM/NEO PI-R taxonomy facilitating its computational implementation, we propose a model for the expression of personality traits in terms of so-called influence operators that add meta control rules to the cycle of rational BDI agents. We distinguish eight different classes of influence operators, depending on the step of the deliberation cycle that they influence and on the operand to which they are applied (goals, actions, plans or functions). Concrete examples are provided through a complete definition of the operators necessary to express one of the personality traits of the Five Factor Model, Conscientiousness, resulting in a set of 30 operators. Finally, we discuss the way to design a specific character through the use of an activation matrix, providing values for each scheme and operator.

Motivated by applications in Dynamic Spectrum Access Networks, we focus on a system in which a few agents are engaged in a costly individual exploration process where each agent's benefit is determined according to the minimum obtained value. Such an exploration pattern is applicable to many systems, including shipment and travel planning. This paper formally introduces and analyzes a sequential variant of the general model. According to that variant, only a single agent engages in exploration at any given time, and when an agent initiates its exploration, it has complete information about the minimum value obtained by the other agents so far. We prove that the exploration strategy of each agent, according to the equilibrium of the resulting Stackelberg game, is reservation-value based, and show how the reservation values can be calculated. We also analyze the agents' expected-benefit maximizing exploration strategies when they are fully cooperative (i.e., when they aim to maximize the expected joint benefit). The equilibrium strategies and the expected benefit of each agent are illustrated using a synthetic homogeneous environment, thereby demonstrating the properties of this new exploration scheme and the benefits of cooperation.

Distributed data collections are now more and more common due to the emergence of cloud computing, to spatially decentralized businesses, or to the availability of various data sharing web services. Obtain knowledge in such a collection raises then the need of new data mining methods to apply in a decentralized architecture. In this paper, we explore a machine learning side of this work direction. We propose a novel technique for decentralized estimation of probabilistic mixture models, which are among the most versatile generative models for understanding data sets. More precisely, we demonstrate how to estimate a global mixture model from a set of local models. Our approach accommodates dynamic topology and data sources and is statistically robust, i.e. resilient to the presence of unreliable local models. Such outlier models may arise from local data which are outliers, compared to the global trend, or poor mixture estimation. We report experiments on synthetic data and real geo-location data from Flickr.

Service selection is one of the important problems in applications of multi-agent systems. A qualitative way is desirable for service selection according to agents' preferences on non-functional Quality of Service (QoS) attributes of services. However, it is challenging when the decision has to be made for multiple agents with preferences on attributes that may be incomplete. In this paper, first use a qualitative graphical representation tool called CP-nets to describe preference relations in a relatively compact, intuitive and structured manner. We then propose a preference reasoning algorithm to construct a derivation tree from a CP-net for each agent and then generate all service patterns for this agent. The Rank semantic is used together with the Lex semantic to provide the ordering of different service patterns. After that, we merge ranked service patterns for all agents and select a set of services that satisfy these agents the most. Finally, we also propose a semantic validation algorithm to show the satisfactory degree of the best service patterns according to other voting semantics. Experimental results indicate that this method can always obtain optimal outcomes which closely satisfy all agents, within acceptable execution time.

The search for information on the Web of Data is becoming increasingly difficult due to its considerable growth. Especially novice users need to acquire both knowledge about the underlying ontology structure and proficiency in formulating formal queries (e.g. SPARQL queries) to retrieve information from Linked Data sources. So as to simplify and automate the querying and retrieval of information from such sources, this paper presents an approach for constructing SPARQL queries based on user-supplied keywords. Our approach utilizes a set of predefined basic graph pattern templates for generating adequate interpretations of user queries. This is achieved by obtaining ranked lists of candidate resource identifiers for the supplied keywords and then injecting these identifiers into suitable positions in the graph pattern templates. The main advantages of our approach are that it is completely agnostic of the underlying knowledge base and ontology schema, that it scales to large knowledge bases and is simple to use. We evaluate all 17 possible valid graph pattern templates by measuring their precision and recall on 53 queries against DBpedia. Our results show that 8 of these basic graph pattern templates return results with a precision above 70%. Our approach is implemented as a Web search interface and performs sufficiently fast to provide answers within an acceptable time frame even when used on large knowledge bases.