Providing a complete and accurate domain model for an agent situated in a complex environment can be an extremely difficult task. Actions may have different effects depending on the context in which they are taken, and actions mayor may not induce their intended effects, with the probability of success again depending on context. In addition, the contexts and probabilities that govern the effec...

In order to collaborate with people in the real world, cognitive systems must be able to represent and reason about spatial regions in human environments. Consider the command “go to the front of the classroom”. The spatial region mentioned (the front of the classroom) is not perceivable using geometry alone. Instead it is defined by its functional use, implied by nearby objects and their confi...

We are concerned with the problem of defining a complex, hybrid, agent based discrete system in a modular way. The modularity results from looking at the system from a number of different perspectives, each dealing with a specific aspect of the system. As a solution a synchronization operator is proposed which glues agent aware systems on shared agents and transitions. The construction turns ou...

Recently, industry has adopted multimodal, context-aware applications. However, addressing various modalities on heterogeneous platforms implies a demanding development effort. Therefore, we present a task-centric methodology and a tool chain leveraging the development of adaptive multimodal applications. In order to improve efficiency the tool chain is based on the Model Driven Architecture ap...

Conceptual information modeling is a well-established practice, aimed at preparing the implementation of information systems, the specification of electronic message formats, and the design of information processes. Today’s ever more connected world however poses new challenges for conceptual information models, as different models should enable mutual connection and reconciliation, even when d...

We present a novel generative model for natural language tree structures in which semantic (lexical dependency) and syntactic (PCFG) structures are scored with separate models. This factorization provides conceptual simplicity, straightforward opportunities for separately improving the component models, and a level of performance comparable to similar, non-factored models. Most importantly, unl...

We present a new probabilistic model based on the lexical PCFG model, which can easily utilize the Chinese character information to solve the lexical information sparseness in lexical PCFG model. We discuss in particular some important features that can improve the parsing performance, and describe the strategy of modifying original label structure to reduce the label ambiguities. Final experim...

In this paper, we present our research on dialog dependent language modeling. In accordance with a speech (or sentence) production model in a discourse we split language modeling into two components; namely, dialog dependent concept modeling and syntactic modeling. The concept model is conditioned on the last question prompted by the dialog system and it is structured using n-grams. The syntact...

The flexibility and unbounded expressivity of our linguistic abilities is unparalleled in the biological world. Explaining how children acquire this fundamental aspect of human language is a key challenge for cognitive science. A recent corpus study by Yang (2013) has cast doubt on the lexical specificity of children’s productivity, as hypothesized by usage-based approaches. Focusing on determi...

The area of probabilistic phrase structure parsing has been a central and active field in computational linguistics. Stochastic methods in natural language processing, in general, have become very popular as more and more resources become available. One of the main advantages of probabilistic parsing is in disambiguation: it is useful for a parsing system to return a ranked list of potential sy...