Smart tourist information points by combining agents, semantics and AI techniques

The tourism sector in the province of Teruel (Aragon, Spain) is increasing rapidly. Although the number of domestic and foreign tourists is continuously growing, there are some tourist attractions spread over a wide geographical area, which are only visited by a few people at specific times of the year. Additionally, having human tourist guides everywhere and speaking different languages is unfeasible. An integrated solution based on smart and interactive Embodied Conversational Agents (ECAs) tourist guides combined with ontologies would overcome this problem. This paper presents a smart tourist information points approach which gathers tourism information about Teruel, structured according to a novel lightweight ontology built on OWL (Ontology Web Language), known as TITERIA (Touristic Information of TEruel for Intelligent Agents). Our proposal, which combines TITERIA with the Maxine platform, is capable of responding appropriately to the users thanks to its Artificial Intelligence Modeling Language (AIML) database and the AI techniques added to Maxine. Preliminary results indicate that our prototype is able to inform users about interesting topics, as well as to propose other related information, allowing them to acquire a complete information about any issue. Furthermore, users can directly talk with an artificial actor making communication much more natural and closer

A multi-agent intelligent system for detecting unknown adverse drug reactions through communication and collaboration

Several thousands of drugs are currently available on the U.S. market. A complete understanding of the safe use of drugs is not possible at the time when drug is developed or marketed. At that time, the safety information is only obtained from a few thousand people in a typical pre-marketing clinical trial. Clinical trials are not capable of detecting rare adverse drug reactions (ADRs) because of limitations in sample size and trial duration. Early detection of unknown ADRs could save lives and prevent unnecessary hospitalizations. Current methods largely rely on spontaneous reports which suffer from serious underreporting, latency, and inconsistent reporting. Thus they are not ideal for rapidly identifying rare ADRs. In this dissertation, I developed a team-based multi-agent intelligent system approach for proactively detecting potential ADR signal pairs (i.e., potential links between drugs and apparent adverse reactions). The basic idea is that intelligent agents are capable of collaborating with one another by sharing information and knowledge which will accelerate the process of detecting ADR signal pairs. Each agent is equipped with a fuzzy inference engine, which enables it to find the causal relationship between a drug and a potential ADR (i.e., a signal pair). The fuzzy inference uses detection rules developed by me in this dissertation. The detection rules are based on different factors. I have also developed a methodology to find similar patients in the multi-agents system. The developed methodology uses similarity fuzzy rules in order to find similar patients in each agent\u27s patient database. In this dissertation, I developed a cooperative learning mechanism that was used by the agents in identifying ADR signal pairs and finding similar patients. The basic idea is that the agents are capable of collaborating with one another by sharing their knowledge. The agents start collaboration by providing their knowledge (i.e. rules) to the other agents. Using confidence level, the most important and insightful detection rules will be found and used for the benefit of the entire agent system. The new updated rules will lead to improve the agents\u27 decision performance. To evaluate our approach, I designed a four-agent system and implemented it using JADE and FuzzyJess software packages. I choose four because it is representative enough while computing time is still reasonable. To assess the performance of the developed system, I conducted two simulation experiments that involved over 20,000 patients treated at the Veterans Affairs Medical Center in Detroit between 2005 and 2008. From the software standpoint, the four agents collaboratively worked one another as designed. Two physicians on the team independently reviewed the multi-agent system results. The results indicate that the agents can successfully collaborate in finding ADR signal pairs and finding similar patients