In this paper, we present a unified model for dialogue understanding involving various sorts of ambiguities, such as lexical, syntactic, semantic, and plan ambiguities. This model is able to estimate and revise the most preferable interpretation of utterances as a dialogue progresses. The model's features successfully capture the dynamic nature of dialogue management. The model consists of two main portions: (1) an extension of first-order logic for maintaining multiple interpretations of ambiguous utterances in a dialogue; (2) a device which estimates and revises the most preferable interpretation from among these multiple interpretations. Since the model is logic-based, it provides a good basis for formulating a rational justification of its current interpretation, which is one of the most desirable aspects in generating helpful responses. These features (contained in our model) are extremely useful for interactive dialogue management.

This paper focuses on the potential value and future prospects of using virtual reality (VR) technology in online education. In detailing online education and the latest VR technology, we focus on metaverse construction and artificial intelligence (AI) for educational VR use. In particular, we describe a virtual university campus in which on-demand VR lectures are conducted in virtual lecture halls, automated evaluations of student learning and training using machine learning, and the linking of multiple digital campuses.

In this paper, we present a new technique for the semantic analysis of sentences, including an ambiguity-packing method that generates a packed representation of individual syntactic and semantic structures. This representation is based on a dependency structure with constraints that must be satisfied in the syntax-semantics mapping phase. Complete syntax-semantics mapping is not performed until all ambiguities have been resolved, thus avoiding the combinatorial explosions that sometimes occur when unpacking locally packed ambiguities. A constraint satisfaction technique makes it possible to resolve ambiguities efficiently without unpacking. Disambiguation is the process of applying syntactic and semantic constraints to the possible candidate solutions (such as modifiees, cases, and wordsenses) and removing unsatisfactory condidates. Since several candidates often remain after applying constraints, another kind of knowledge to enable selection of the most plausible candidate solution is required. We call this new knowledge a preference. Both constraints and preferences must be applied to coordination for disambiguation. Either of them alone is insufficient for the purpose, and the interactions between them are important. We also present an algorithm for controlling the interaction between the constraints and the preferences in the disambiguation process. By allowing the preferences to control the application of the constraints, ambiguities can be efficiently resolved, thus avoiding combinatorial explosions.