In human robot dialogue, identifying intended referents from human partners’ spatial language is challenging. This is partly due to automated inference of potentially ambiguous underlying reference system (i.e., frame of reference ). To improve spatial language understanding, we conducted an empirical study to investigate the prevalence of ambiguities of frame of reference. Our findings indicate that ambiguities do arise frequently during human robot dialogues. Although situational factors from the spatial arrangement are less indicative for the underlying reference system, linguistic cues and individual preferences may allow reliable disambiguation.

This paper aims at bringing social robots closer to naive users. A Natural Programming System that allows the end-user to give instructions to a Social Robot has been developed. The instructions derive in a sequence of actions and conditions, that can be executed while the own sequence verbal edition continues. A Dialogue Manager System (DMS) has been developed in a Social Robot. The dialog is described in a voiceXML structure, where a set of information slots is defined. These slots are related to the necessary attributes for the construction of the sequence in execution time. The robot can make specific requests on encountering unfilled slots. Temporal aspects of dialog such as barge-in property, mixed-initiative, or speech intonation control are also considered. Dialog flow is based on Dialog Acts. The dialog specification has also been extended for multimodality management. The presented DMS has been used as a part of a Natural Programming System but can also be used for other multimodal humanrobot interactive skills.

This paper presents a robust methodology for grounding vocabulary in robots. A social language grounding experiment is designed, where, a human instructor teaches a robotic agent the names of the objects present in a visually shared environment. Any system for grounding vocabulary has to incorporate the properties of gradual evolution and lifelong learning. The learning model of the robot is adopted from an ongoing work on developing systems that conform to these properties. Significant modifications have been introduced to the adopted model, especially to handle words with multiple meanings. A novel classification strategy has been developed for improving the performance of each classifier for each learned category. A set of six new nearest-neighbor based classifiers have also been integrated into the agent architecture. A series of experiments were conducted to test the performance of the new model on vocabulary acquisition. The robot was shown to be robust at acquiring vocabulary and has the potential to learn a far greater number of words (with either single or multiple meanings).

We outline several challenges and opportunities for building physically situated systems that can interact in open, dynamic, and relatively unconstrained environments. We review a platform and recent progress on developing computational methods for situated, multiparty, open-world dialog, and highlight the value of representations of the physical surroundings and of harnessing the broader situational context when managing communicative processes such as engagement, turn taking, language understanding, and dialog management. Finally, we outline an open-world learning challenge that spans these different levels

Among the many aspects of human intelligence that currently elude the simulation by machines is that of story understanding. Although many theories of narrative have been proposed, several processes pertaining to narrative remain inadequately formalized and, hence, beyond full mechanization. This work proposes a general formal framework that attempts to make precise such processes and related notions, with first and foremost that of what constitutes a narrative. Emphasis is placed on identifying certain premises that narratives are expected to adhere to, and deriving the formal implications that these have in terms of the computability of the various relevant notions. Among others, it is established that checking whether a discourse is a narrative is decidable, and that narratives can be computably enumerated and, hence, unambiguously indexed.

Lehnert's Plot Units (Lehnert 1981) or Rumelhart's Story Grammars (Rumelhart 1980), and naturally, one would like We give semiformal We aim at capturing the informal human notion of equivalence definitions in § 2 and then give a few examples (without any of stories in a formal system in such a way that formal details) in § 3. two stories are perceived as equivalent when their formal representations are isomorphic (cf. There is no unique "human Comparing the adequacy of frameworks is not a formal task, notion of equivalence of stories" as the research on analogical but deals with the degree of representation of the informal reasoning shows (Rattermann and Gentner 1987; notions in the formal setting.

This paper proposes a number of social issues that are essential in understanding any given story, and thus, that must be included in a comprehensive approach to computational modeling of narrative. It focuses on oral narratives, and on the social event of the telling of a story. For participants in the telling, the central social issue is the story’s evaluation or meaning: the point or moral of the story. Value or meaning is created relative to social membership, and so, to understand evaluation, it is not sufficient to understand a story solely as a bounded unit. Therefore, this paper examines the ways in which narrative meaning is negotiated between narrator and interlocutors. It demonstrates how a given story can take on different meanings for different audiences. The life course of a story is also proposed as relevant dimension for understanding. Ephemeral stories are distinguished from stories which have multiple tellings, both for the stories of individuals, and for stories which form part of the story stock of institutions. Storytelling rights are also considered: who within a group has the right to tell a particular story on a particular occasion. These issues are proposed as potential meta-data to be used in the analysis of stories. Finally, the paper indicates an area in which computational understanding of narrative, including these social issues, has potential for practical applications: as part of current commercial knowledge capture and archiving activities.

Narratives structure our understanding of the world and of ourselves. They exploit the shared cognitive structures of human motivations, goals, actions, events, and outcomes. We report on a computational model that is motivated by results in neural computation and captures fine-grained, context sensitive information about human goals, processes, actions, policies, and outcomes. We describe the use of the model in the context of a pilot system that is able to interpret simple stories and narrative fragments in the domain of international politics and economics. We identify problems with the pilot system and outline extensions required to incorporate several crucial dimensions of narrative structure.

This position paper presents an approach to computational narrative based in cognitive linguistics and sociolinguistics accounts of conceptual blending, metaphor, and narrative, multimedia semantics, human-centered interface design, and digital media art practice. In particular, as a case study, we describe the Living Liberia Fabric, an AI-based interactive narrative system developed in affiliation with the Truth and Reconciliation Commission (TRC) of Liberia to memorialize a fourteen-year civil war. The Living Liberia Fabric project is led by Fox Harrell and executed in the Imagination, Computation, and Expression (ICE) Laboratory at Georgia Tech. The system exemplifies a cultural computing approach (grounding computing practices in a wider range of specific cultural traditions and values than those that are privileged in computer science).

Processing the language of a narrative text, be it a novel, a extended flashbacks). These subsequent levels of blending film, or a play, is a crucial component of narrative of narrative spaces eventually yield the emergent space, comprehension. The research reported here shows how traditionally described as'the story'. The final product of processes driven by general linguistic and conceptual narrative comprehension is thus a mental construct, a patterns of meaning construction prompt the reader's or mega-blend, which emerges through multiple levels of viewer's response to the narrative artifact.