Generic Command Interpretation Algorithms for Conversational Agents

Mazuel, Laurent

doi:10.1109/iat.2006.70

Cited by 6 publications

(4 citation statements)

References 15 publications

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“…The expert should know what kind of relation is important for semantic proximity. Since we consider the application of our semantic heterogeneity system in semantic interpretation of natural language commands (considering that an user is a special agent [29]), we think that the user feedback can be used as a background knowledge for a reinforcement learning algorithm [30,31] on the weight evolution. When the user confirms the system's interpretation of the command (i.e.…”

Section: Discussionmentioning

confidence: 99%

Semantic Relatedness Measure Using Object Properties in an Ontology

Mazuel¹,

Sabouret²

2008

Lecture Notes in Computer Science

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Abstract. This paper presents a new semantic relatedness measure on ontologies which considers especially the object properties between the concepts. Our approach relies on two hypotheses. Firstly, using only concept hierarchy and object properties, only a few paths can be considered as "semantically corrects" and these paths obey to a given set of rules. Secondly, following a given edge in a path has a cost (represented as a weight), which depends on its type (is-a, part-o f , etc.), its context in the ontology and its position in this path. We propose an evaluation of our measure on the lexical base WordNet using part-o f relation with two different benchmarks. We show that, in this context, our measure outperforms the classical semantic measures.

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Section: Discussionmentioning

confidence: 99%

Semantic Relatedness Measure Using Object Properties in an Ontology

Mazuel¹,

Sabouret²

2008

Lecture Notes in Computer Science

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“…The agent rewrites the tree at every execution step according to its specific elements. This model allows agents to access at runtime to the description of their actions, and to reason about it for planning, formal question answering and behavior recognition [13]. Agents can interact with each other by sending messages.…”

Section: Multi-agents Architecturementioning

confidence: 99%

Dynamic Service Composition in Ambient Intelligence Environment

Benazzouz

Chikhaoui

2009

2009 IEEE International Conference on Services Computing

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In Ambient Intelligence (AmI) environments, some services provided by AmI devices are often not visible to users and to other devices. The existing approaches deal with services' composition and discovery as two independent parts. In this paper, we propose an alternative approach based on logical reasoning agent system. This system is supported by a communication protocol where agents discover automatically services provided in their environment and construct dynamically composite services. The service composition is constructed from an exchange of idiomatic expressions among agents and users, while the discovery process takes the form of an information request via the communication protocol. The advantage of this approach is that agents are able to acquire knowledge from each other and when interacting with users. This capability will facilitate the satisfaction of user's requirements in an intelligent way. This study shows that agents are able to satisfy new services previously unknown to the system.

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“…Nowadays, conversational agents tend to be experts in specific domains and interact with the users in a natural language sufficient for that domain [4,25,40]. Some researchers try to develop general architectures for designing specialized natural language understanding (NLU) units [27]. NLU unit recognizes user's intentions based on information arrived from text refiner and syntactic parser.…”

Section: Agent Architecture Componentsmentioning

confidence: 99%

Architectural design and implementation of intelligent embodied conversational agents using fuzzy knowledge base

Hassani

Nahvi

Ahmadi

2013

Journal of Intelligent &Amp; Fuzzy Systems

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In this paper, a general architecture is proposed for developing embodied conversational agents with fuzzy ontology knowledge base. The proposed architecture enables agents to interact with the user via multimodal channels in a virtual reality environment for the purpose of language learning. The agents play the role of emotional, rational, and friendly partners who provide a specific domain of knowledge based on user's queries in natural language. These queries are performed by an optimized fuzzy search engine. Two scenarios including two virtual airports and a virtual electronic gadget shop are implemented in this architecture to improve users' oral skills. The results show the users' average oral skills improved 11%. Moreover, 80% of the users ranked agents' logical sequence of actions and the total speed of responses as very good, and 90% of them evaluated agents' appropriateness of responses as very good based on Likert scale. ECAs have been implemented on a vast variety of platforms varying from mobile services [6, 32] to the World Wide Web [29, 33]. These sociable agents have been exploited in many service domains such as e-commerce [7, 25, 40], tourism query systems [2, 3], entertainment [31], and pedagogical ECAs [4, 21] for Computer-Assisted Language Learning (CALL) [13, 35]. The principal categories of ECA research include believability, social interface, application domains, computational issues, and production [24]. Many case studies show advantages of ECAs in enhancing problem solving skills, showing higher levels of motivation and interest, improving learning [30], having positive effect on memory performance [36], decreasing the intensity of user's negative emotions [19], etc. It is shown that ECAs have the role of a conversational partner and this effect is permanent [30].

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Generic Command Interpretation Algorithms for Conversational Agents

Cited by 6 publications

References 15 publications

Semantic Relatedness Measure Using Object Properties in an Ontology

Semantic Relatedness Measure Using Object Properties in an Ontology

Dynamic Service Composition in Ambient Intelligence Environment

Architectural design and implementation of intelligent embodied conversational agents using fuzzy knowledge base

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