In robotics research with language-based interaction, simplifications are made, such that a given event can be described in a unique manner, where there is a direct mapping between event representations and sentences that can describe these events. However, common experience tells us that the same physical event can be described in multiple ways, depending on the perspective of the speaker. The current research develops methods for representing events from multiple perspectives, and for choosing the perspective that will be used for (...) generating a linguistic construal, based on attentional processes in the system. The multiple perspectives are based on the principle that events can be considered in terms of the force driving the event, and the result obtained from the event, based on the theory of Gärdenfors. In addition, within these perspectives a further refinement can be made with respect to the agent, object, and recipient perspectives. We develop a system for generating appropriate construals of meaning, and demonstrate how this can be used in a realistic dialogic interaction between a behaving robot and a human interlocutor. (shrink)

How do people learn to talk about the causal and temporal relations between events, and the motivation behind why people do what they do? The narrative practice hypothesis of Hutto and Gallagher holds that children are exposed to narratives that provide training for understanding and expressing reasons for why people behave as they do. In this context, we have recently developed a model of narrative processing where a structured model of the developing situation is built up from experienced events, and (...) enriched by sentences in a narrative that describe event meanings. The main interest is to develop a proof of concept for how narrative can be used to structure, organize and describe experience. Narrative sentences describe events, and they also define temporal and causal relations between events. These relations are specified by a class of narrative function words, including “because, before, after, first, finally.” The current research develops a proof of concept that by observing how people describe social events, a developmental robotic system can begin to acquire early knowledge of how to explain the reasons for events. We collect data from naïve subjects who use narrative function words to describe simple scenes of human-robot interaction, and then employ algorithms for extracting the statistical structure of how narrative function words link events in the situation model. By using these statistical regularities, the robot can thus learn from human experience about how to properly employ in question-answering dialogues with the human, and in generating canonical narratives for new experiences. The behavior of the system is demonstrated over several behavioral interactions, and associated narrative interaction sessions, while a more formal extended evaluation and user study will be the subject of future research. Clearly this is far removed from the power of the full blown narrative practice capability, but it provides a first step in the development of an experimental infrastructure for the study of socially situated narrative practice in human-robot interaction. (shrink)

Heyes does well to argue that some of the apparently innate human capabilities for cultural learning can be considered in terms of more general-purpose mechanisms. In the application of this to language, she overlooks some of its most interesting properties. I review three, and then illustrate how mindreading can come from general-purpose mechanism via language.

A significant body of literature has identified how narrative provides a basis for perceiving and understanding human experience. In the target article, the authors arrive at the need for a form of narrative-based reasoning due to constraints that render probabilistic-based reasoning ineffective. This commentary attempts to bridge this gap and identify links between the proposed and existing theories.

Recognizing limitations of the “syntactocentric” perspective, Jackendoff proposes a model in which phonology, syntax, and conceptual systems are each independently combinatorial. We can ask, however, whether he has taken this issue to its logical conclusion. The fundamental question that is not fully addressed is whether the combinatorial aspect of syntax originated in, and derives from, the indeed “far richer” conceptual system, a question to be discussed.

As there is in the neuroscience of individuals engaged in dynamic interactions, similar dark matter is present in the domain of interaction between humans and cognitive robots. Progress in second-person neuroscience will contribute to the development of robotic cognitive systems, and such developed robotic systems will be used to test the validity of the underlying theories.

The development of reasoning systems exploiting expert knowledge from interactions with humans is a non-trivial problem, particularly when considering how the information can be coded in the knowledge representation. For example, in human development, the acquisition of knowledge at one level requires the consolidation of knowledge from lower levels. How is the accumulated experience structured to allow the individual to apply knowledge to new situations, allowing reasoning and adaptation? We investigate how this can be done automatically by an iCub that (...) interacts with humans to acquire knowledge via demonstration. Once consolidated, this knowledge is used in further acquisitions of experience concerning preconditions and consequences of actions. Finally, this knowledge is translated into rules that allow reasoning and planning for novel problem solving, including a Tower of Hanoi scenario. We thus demonstrate proof of concept for an interaction system that uses knowledge acquired from human interactions to reason about new situations. (shrink)