Emotions, conversational systems and heterogeneous data sources

Eduardo M. Eisman Cabeza

Ayuda

Emotions, conversational systems and heterogeneous data sources

Autores: Eduardo M. Eisman Cabeza
Directores de la Tesis: Juan Luis Castro Peña (dir. tes.)
Lectura: En la Universidad de Granada ( España ) en 2015
Idioma: español
Tribunal Calificador de la Tesis: Juan Carlos Cubero Talavera (presid.) , Héctor Pomares Cintas (secret.) , Alejandro Moreo Fernández (voc.) , Pablo Carmona del Barco (voc.) , María Isabel Navarro Jiménez (voc.)
Enlaces
- Tesis en acceso abierto en: DIGIBUG
Resumen
- Conversational agents are intelligent systems, usually represented by a real character or a cartoon, which are able to engage in a certain natural language conversation with a human being, interact with their environment, and behave as a real person would do. The main objective of this technology is to make human-computer interaction easier. It can be used in any situation where there is a communication among people, with the agent playing the role of any of them. Nowadays, conversational agents are being more and more used in different application domains. From agents that simulate the behavior of real people for training on different fields, to systems that act like natural language query interfaces to large information sources. In short, an endless number of areas such as culture, entertainment, tourism, e-learning, e-commerce, or medicine, can greatly benefit from using this technology.
  
  From the users¿ point of view, many studies (Kaufmann and Bernstein, 2010; Zhou et al., 2012) reveal that there is a clear preference for natural language interfaces to the detriment of others more traditional like keywords search of classic search engines, formal query languages, or menu driven interaction. In addition, it has been demonstrated that the interest of users decreases exponentially with the increase in the number of mouse clicks, fact that is emphasized even more if we talk about mobile devices where traditional input interfaces are very limited. However, natural language systems do are able to ease and improve the user experience.
  
  This thesis is not exclusively theoretical, but it has a strong practical component. In fact, it provides solutions to three real problems demanded by information society, within the field of conversational agents and the world of natural language processing, which we have faced in different projects.
  
  First of all, the improvement of the naturalness of this kind of systems. This problem is motivated by the increasing interest in the development of intelligent systems that simulate the behavior of human beings. These agents must behave with a high realism level, so they must be able to emotionally react to the events that happen in the world they live. This highlights the need for an emotional state control system that could guarantee that agents behave in a consistent way and adapt themselves to the different situations naturally. Although there are some emotional state control systems (Yanaru et al., 1994; El-Nasr et al., 2000; Egges et al., 2003), non-covered or improvable necessities (realism, gradualness, personality, emotional filtering, temporal decay, reuse, adaptability, interpretability, non-determinism, emotional stability, memory, conduct types, agents¿ interaction, agent¿s health¿) made evident the importance of developing a new system that could solve our problem with total accuracy.
  
  In second place, the problem of information overload that there is nowadays on the Internet. There are certain websites which, in spite of having a good structure, organization and design, handle such amount of information that sometimes it is complicated to find a certain information in which we can be interested. In this sense, it was evident the necessity of defining a new mechanism for organizing and accessing the information that was highly efficient and effective. It is here where virtual assistants have proven to be the most effective technology in recent years because they are able to interpret and answer complex natural language questions. Although the first systems existing in literature could be considered more as tools that did not allow asking natural language questions (Lieberman, 1995), little by little these systems were gaining popularity (Microsoft¿s agents, Ball et al., 1997) and using more advanced techniques such as case-based reasoning, neural networks or graphs (Wexelblat and Maes, 1999), and they kept evolving and including features similar to the ones that we can find in conversational agents nowadays (Cassell et al., 2000). However, the missing and improvable features (e.g. the lack of a virtual character able to show emotions that make the interaction process friendlier, the ability to answer general domain questions in several languages, generate dynamic answers whose content depends on certain restrictions, or guide the user through a certain process) made us propose a methodology and a framework for the design of closed-domain virtual assistants which could be integrated into every existing website and would cover the features that our problem required.
  
  Finally, the third problem that we have faced is the access to heterogeneous data sources. Nowadays there are more and more applications that integrate the information from different origins or services in order to provide a solution to a problem. However, many times the way of accessing that information turns out to be complicated, not very natural and inefficient for users. This situation happens in many e-commerce portals. To solve this problem, we needed a natural language system that could be able to integrate, transparently for the user, the knowledge existing in several independent data sources, each of which could have a different format and structure. It had to be interactive, since sometimes the requests of users turn out to be a little bit vague in the sense that they need to be completed little by little with additional information, which could be perfectly provided in a dialog with the system. Likewise, it was essential that it could handle fuzzy concepts such as ¿cheap¿ or ¿recent¿ and temporal queries that involve relative dates. Finally, the system had to be able to advise users according to their preferences. The majority of existing systems are focused on a very specific part of the problem. Some like NaLIR (Li and Jagadish, 2014b) have interesting features as natural language interfaces. They support complex SQL queries, but they use just one knowledge source and lack conversational capabilities. Others, like ORAKEL (Cimiano et al., 2008), focus on minimizing the effort of adapting the system to a given domain. On the other hand, systems like Natural Language Assistant (Chai et al., 2002) do include a dialog manager, but they neither support general domain queries nor work with different types of data sources at the same time. In the case of a commercial system like IKEA¿s Anna, she is much more complete in that conversational side, but not so advanced in the interaction with the database (she does not handle fuzzy or temporal queries), and she does not seem to include a full emotional model. These and other problems such as the cleansing and filtering of information from the database, the use of taxonomies that enrich that information, or the inclusion of an advising module to advise users in a personalized way, are the ones that we try to solve with the design of a new system for accessing heterogeneous data sources.
  
  In this way, the main objective of this thesis is to provide solutions to a series of interesting open problems related to the domain of conversational systems:
  
  ¿ To make an emotion modeling system that is easy to interpret and modify, and improves the naturalness of conversational agents. Our hypothesis is that the application of fuzzy rule-based systems would provide a good solution to this problem (Eisman et al., 2009).
  
  ¿ To make easy the access, in an immediate and precise way, to great amounts of dynamic information related to a certain domain. We believe that the use of semantic structures such as ontologies and the application of rules and restrictions during the decision making process would allow organizing and taking advantage of all the existing knowledge in the domain (Eisman et al., 2012).
  
  ¿ To combine different heterogeneous data sources to allow users to access information in an integrated, transparent, effective, efficient, and pleasant way. We consider that the use of multi-agent systems able to combine expert agents and decision agents would allow solving this problem in a modular and scalable way (Eisman et al., 2015).
  
  Moreover, in order to achieve these objectives not only theoretically, we have considered to develop systems of commercial interest which put the acquired knowledge and the designed models into practice, and can be used to solve real problems.
  
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