Knowledge-based system to define context in commercial applications

Applied Artificial Intelligence

Molina

2013

During recent years, conversational agents have become a solution to provide straightforward and more natural ways of retrieving information in the digital domain. In this article, we present an agent based dialog simulation technique for learning new dialog strategies and evaluating con versational agents. Using this technique, the effort necessary to acquire data required to train the dialog model and then explore new dialog strategies is considerably reduced. A set of measures has also been defined to evaluate the dialog strategy that is automatically learned and to compare different dialog corpora. We have applied this technique to explore the space of possible dialog stra tegies and evaluate the dialogs acquired for a conversational agent that collects monitored data from patients suffering from diabetes. The results of the comparison of these measures for an initial corpus and a corpus acquired using the dialog simulation technique show that the conversational agent reduces the time needed to complete the dialogs and improve their quality, thereby allowing the conversational agent to tackle new situations and generate new coherent answers for the situations already present in an initial model.

Section: Our Agent-based Dialog Simulation Techniquementioning

confidence: 99%

An Automatic Dialog Simulation Technique to Develop and Evaluate Interactive Conversational Agents

Applied Artificial Intelligence

Molina

2013

“…A Facilitator Agent links the different positions to the providers and services defined in the system. A Positioning Agent communicates with the ARUBA positioning system [8] to extract and transmit positioning information to other agents in the system. Finally, a Log Analyzer Agent generates user profiles that are used by Conversational Agents to adapt their behaviour taking into account the preferences detected in the users' previous dialogues.…”

Section: Our Agent System To Provide Context-aware Servicesmentioning

confidence: 99%

“…We compare them using different types of messages for a common domain: Airport presented in [8]. In previous work we design an architecture that has three types of agents: Central Agent, Provider Agents and User Agents [2].…”

Section: Practical Application: the Case Of An Airport Domainmentioning

confidence: 99%

“…We take experimentation using "Service Recommendation" in an Airport Domain [8] In order to assign weights to each type of messages, we follow this criteria: if there is any interest over a negotiation from the Users side, then the maximum weight is assigned to the Agree-proposal or Reject-proposal messages; if it is the system who tries to recommend a service based on its behavior, the maximum weight is assigned to Inform-ref or Propose messages. In the case of MAS-CONVERSATIONAL-AGENT, the Inform-ref message belongs to Phase 12 of our MAS interaction and would have a maximal weight because it is the systems intention to recommend a service based on the behaviour of the passenger.…”

Section: Practical Application: the Case Of An Airport Domainmentioning

confidence: 99%

See 1 more Smart Citation

Evaluating Interaction of MAS Providing Context-Aware Services

Sánchez-Pi

Agent and Multi-Agent Systems: Technologies and Applications

et al. 2011

Self Cite

Abstract. Much effort has been spent on suggesting and implementing new ar-chitectures of MAS to specific domains. Often each new architecture is not even compared to any existing architectures in order to evaluate their potential ben-efits. The evaluation of Multi-Agent Systems (MAS) is a complex problem and it does not have a single form. The present work follows the research line of considering the agent interaction as the main evaluation criteria, the most impor-tant characteristic of any complex software as autonomous agents according to [9]. So, in this paper, we have suggested an assignment of evaluation values to Agents interaction in an specific MAS architecture for providing context services by means of conversational agents. This evaluation is mainly based on the rele-vance of the messages content brought by an interaction. For dependant nature of the relevance of the messages, the valuation has to be adhoc, but our paper provides an example of how interesting is this alternative in order to evaluate any MAS architecture theoretically.

“…A Facilitator Agent links the different positions to the providers and services defined in the system. A Positioning Agent communicates with the ARUBA positioning system [7] to extract and transmit positioning information to other agents in the system. Finally, a Log Analyzer Agent generates user profiles that are used by Conversational Agents to adapt their behaviour taking into account the preferences detected in the users' previous dialogs.…”

Section: Our Proposed Multi-agent Architecturementioning

confidence: 99%

An Architecture to Provide Context-Aware Services by Means of Conversational Agents

Sánchez-Pi

Advances in Intelligent and Soft Computing

et al. 2010

Abstract. In human-human interaction, a great deal of information is conveyed without explicit communication. This context information characterizes the situation of the different entities involved in the communication process (users, place, environment and computational objects). In this paper, we present an agent-based architecture that incorporates this valuable information to provide the most adapted service to the user. One of the main characteristics of our proposal is the incorporation of conversational agents handling different domains and adapted taking into account the different users requirements and preferences by means of a context manager. This way, we ensure a natural communication between the user and the system to provide a personalized service. The implementation of our proposed architecture to develop and evaluate a context-aware railway information system is also described.