José Luis Arjona

A top down approach for MAS protocol descriptions

When the protocol of a complex Multi-Agent System (MAS) needs to be developed, the top-down approach emphasises to start with abstract descriptions that should be refined incrementally until we achieve the detail level necessary to implement it. Unfortunately, there exist a semantic gap in protocol description methodologies because most of them first identify which tasks have to be performed, and then use low level descriptions such as sequences of messages to detail them. In this paper, we propose an approach to bridge this gap. We model MAS protocols using several abstract views of the tasks to be performed, and provide a systematic method to simplify them. Tasks are represented by means of interactions that may be refined into lower-level interactions with the techniques proposed in this paper (simpler interactions are easier to describe and implement using message passing.) Unfortunately, deadlocks may appear due to protocol design mistakes or due to the refinement process. Thus, we also propose an algorithm to ensure that protocols are deadlock free.

From Wrapping to Knowledge

One the most challenging problems for enterprise information integration is to deal with heterogeneous information sources on the Web. The reason is that they usually provide information that is in human-readable form only, which makes it difficult for a software agent to understand it. Current solutions build on the idea of annotating the information with semantics. If the information is unstructured, proposals such as S-CREAM, MnM, or Armadillo may be effective enough since they rely on using natural language processing techniques; furthermore, their accuracy can be improved by using redundant information on the Web, as C-PANKOW has proved recently. If the information is structured and closely related to a back-end database, deep annotation ranges among the most effective proposals, but it requires the information providers to modify their applications; if deep annotation is not applicable, the easiest solution consists of using a wrapper and transforming its output into annotations. In this paper, we prove that this transformation can be automated by means of an efficient, domain-independent algorithm. To the best of our knowledge, this is the first attempt to devise and formalize such a systematic, general solution

Towards Interaction Protocol Operations for Large Multi-agent Systems

It is widely accepted that role-based modelling is quite adequate in the context of multi-agent systems (MAS) modelling techniques. Unfortunately, very little work has been reported on how to describe the relationships between several role models. Furthermore, many authors agree on that protocols need to be encapsulated into high-level abstractions. The synthesis of role models is an operation presented in the OORAM methodology that allows us to build new role models from others in order to represent the interrelations they have. To the best of our knowledge this operation has to be performed manually at protocol level and works with protocols expressed by means of messages. In this paper, we present two algorithms to extract the protocol of a role from the protocol of a role model and vice versa that automate the synthesis or role models at the protocol level. Furthermore, in order to deal with protocol descriptions in a top down approach both operations work with protocols expressed by means of an abstraction call multi-role interaction (mRI).

Automatic Extraction of Semantically-Meaningful Information from the Web.

The semantic Web will bring meaning to the Internet, making it possible for web agents to understand the information it contains. However, current trends seem to suggest that the semantic web is not likely to be adopted in the forthcoming years. In this sense, meaningful information extraction from the web becomes a handicap for web agents. In this article, we present a framework for automatic extraction of semantically-meaningful information from the current web. Separating the extraction process from the business logic of an agent enhances modularity, adaptability, and maintainability. Our approach is novel in that it combines different technologies to extract information, surf the web and automatically adapt to web changes.

Coping with web knowledge

The web seems to be the biggest existing information repository. The extraction of information from this repository has attracted the interest of many researchers, who have developed intelligent algorithms (wrappers) able to extract structured syntactic information automatically. In this article, we formalise a new solution in order to extract knowledge from todays non-semantic web. It is novel in that it associates semantics with the information extracted, which improves agent interoperability; furthermore, it achieves to delegate the knowledge extraction procedure to specialist agents, easing software development and promoting software reuse and maintainability.

An Experiment to Test URL Features for Web Page Classification

Web page classification has been extensively researched, using different types of features that are extracted either from the page content, the page structure or from other pages that link to that page. Using features from the page itself implies having to download it before its classification. We present an experiment to proof that URL tokens contain information enough to extract features to classify web pages. A classifier based on these features is able to classify a web page without having to download it previously, avoiding unnecessary downloads.

A knowledge extraction process specification for today's non-semantic Web

The semantic Web shall enable Web agents an efficient, precise, and comprehensive extraction of knowledge. Nevertheless, this new Web is not likely to be adopted in the immediate future. We present a specification of a new framework in order to extract knowledge from todays dynamics nonsemantic Web. Our proposal is novel in that it associates semantics with the information extracted, which improves agent interoperability; it can also deal with changes to the structure of a Web page, which improves adaptability; furthermore, it achieves to delegate the knowledge extraction procedure to specialist agents, easing software development and promoting software reuse and maintainability.

A Practical Agent-Based Method to Extract Semantic Information from the Web

The semantic Web will bring meaning to the Internet, mak - ing it possible for web agents to understand the information it contains. However, current trends seem to suggest that it is not likely to be adopted in the forthcoming years. In this sense, meaningful information extraction from the web becomes a handicap for web agents. In this article, we present a framework for automatic extraction of semantically-meaningful information from the current web. Separating the extraction process from the business logic of an agent enhances modularity, adaptability, and maintainability. Our approach is novel in that it combines different technologies to extract information, surf the web and automatically adapt to some changes.

An Efficient Orchestration Engine for the Cloud

The Cloud is evolving as a cost-effective solution to run services that support a variety of business processes. It is not surprising then that Orchestration as a Service (OaaS) is gaining importance as a means to integrate the many services a typical company runs or out sources in the Cloud. OaaS requires very efficient orchestration engines: the faster they work, the less customers have to pay and the more customers can be served. In this paper, we report on a new orchestration engine, we have performed a series of stringent experiments that prove that it outperforms a state-of-the-art orchestration engine in widespread use. Our conclusion is that our proposal is an efficient, solid orchestration engine ready for the Cloud.

MAVE: Multilevel wrApper Verification systEm

Wrappers are pieces of software used to extract data from websites and structure them for further application processing. Unfortunately, websites are continuously evolving and structural changes happen with no forewarning, which usually results in wrappers working incorrectly. Thus, wrappers maintenance is necessary for detecting whether wrapper is extracting erroneous data. The solution consists of using verification models to detect whether wrapper output is statistically similar to the output produced by the wrapper itself when it was successfully invoked in the past. Current proposals present some weaknesses, as the data used to build these models are supposed to be homogeneous, independent, or representative enough, or following a single predefined mathematical model. In this paper, we present MAVE, a novel multilevel wrapper verification system that is based on one-class classification techniques to overcome previous weaknesses. The experimental results show that our proposal outperforms accuracy of current solutions.