Towards an Indexical Model of Situated Language Comprehension for Cognitive Agents in Physical Worlds

We propose a computational model of situated language comprehension based on the Indexical Hypothesis that generates meaning representations by translating amodal linguistic symbols to modal representations of beliefs, knowledge, and experience external to the linguistic system. This Indexical Model incorporates multiple information sources, including perceptions, domain knowledge, and short-term and long-term experiences during comprehension. We show that exploiting diverse information sources can alleviate ambiguities that arise from contextual use of underspecific referring expressions and unexpressed argument alternations of verbs. The model is being used to support linguistic interactions in Rosie, an agent implemented in Soar that learns from instruction.Comment: Advances in Cognitive Systems 3 (2014

Mapping web personal learning environments

A recent trend in web development is to build platforms which are carefully designed to host a plurality of software components (sometimes called widgets or plugins) which can be organized or combined (mashed-up) at user's convenience to create personalized environments. The same holds true for the web development of educational applications. The degree of personalization can depend on the role of users such as in traditional virtual learning environment, where the components are chosen by a teacher in the context of a course. Or, it can be more opened as in a so-called personalized learning environment (PLE). It now exists a wide array of available web platforms exhibiting different functionalities but all built on the same concept of aggregating components together to support different tasks and scenarios. There is now an overlap between the development of PLE and the more generic developments in web 2.0 applications such as social network sites. This article shows that 6 more or less independent dimensions allow to map the functionalities of these platforms: the screen dimensionmaps the visual integration, the data dimension maps the portability of data, the temporal dimension maps the coupling between participants, the social dimension maps the grouping of users, the activity dimension maps the structuring of end users–interactions with the environment, and the runtime dimensionmaps the flexibility in accessing the system from different end points. Finally these dimensions are used to compare 6 familiar Web platforms which could potentially be used in the construction of a PLE

Complexity and control: how do they work in PPPs?

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The state-of-the-art in personalized recommender systems for social networking

With the explosion of Web 2.0 application such as blogs, social and professional networks, and various other types of social media, the rich online information and various new sources of knowledge flood users and hence pose a great challenge in terms of information overload. It is critical to use intelligent agent software systems to assist users in finding the right information from an abundance of Web data. Recommender systems can help users deal with information overload problem efficiently by suggesting items (e.g., information and products) that match users’ personal interests. The recommender technology has been successfully employed in many applications such as recommending films, music, books, etc. The purpose of this report is to give an overview of existing technologies for building personalized recommender systems in social networking environment, to propose a research direction for addressing user profiling and cold start problems by exploiting user-generated content newly available in Web 2.0