To be useful teammates to human partners, robots must be able to follow spoken instructions given in natural language. However, determining the correct sequence of actions in response to a set of spoken instructions is a complex decision-making problem. There is a "semantic gap" between the high-level symbolic models of the world that people use, and the low-level models of geometry, state dynamics, and perceptions that robots use. In this paper, we show how this gap can be bridged by inferring the best sequence of actions from a linguistic description and environmental features. This work improves upon previous work in three ways. First, by using a conditional random field (CRF), we learn the relative weight of environmental and linguistic features, enabling the system to learn the meanings of words and reducing the modeling effort in learning how to follow commands. Second, a number of long-range features are added, which help the system to use additional structure in the problem. Finally, given a natural language command, we infer both the referred path and landmark directly, thereby requiring the algorithm to pick a landmark by which it should navigate. The CRF is demonstrated to have 15% error on a held-out dataset, when compared with 39% error for a Markov random field (MRF). Finally, by analyzing the additional annotations necessary for this work, we find that natural language route directions map sequentially onto the corresponding path and landmarks 99.6% of the time. In addition, the size of the referred landmark varies from 0m 2 to 1964m 2 and the length of the referred path varies from 0 m to 40.83 m .

The useful This paper reports on the ongoing work done in the information is obviously multilevel. In this work we are GVLEX project. The aim of this multidisciplinary project not willing to design complete analysis for each level of is to design and test a storytelling humanoid robot. Ideally, interest but rather to design a multilevel analysis able to the robot would be able to process automatically a given point out the interesting parts of the tale. Based on the tale or short story, and to play it for a children audience.

In an object manipulation dialogue, a robot may misunderstand an ambiguous command from a user, such as 'Place the cup down (on the table)," potentially resulting in an accident. Although making confirmation questions before all motion execution will decrease the risk of this failure, the user will find it more convenient if confirmation questions are not made under trivial situations. This paper proposes a method for estimating ambiguity in commands by introducing an active learning framework with Bayesian logistic regression to human-robot spoken dialogue. We conducted physical experiments in which a user and a manipulator-based robot communicated using spoken language to manipulate objects.

Fast mapping is a phenomenon by which children learn the meanings of novel adjectives after a very small number of exposures when the new word is contrasted with a known word. The present study was a preliminary test of whether machine learners could use such contrasts in unconstrained speech to learn adjective meanings and categories. Six decision tree-based learning methods were evaluated that use contrasting examples in order to work toward an adjective fast-mapping system for machine learners. Subjects tended to compare objects using adjectives of the same category, implying that such contrasts may be a useful source of data about adjective meaning, though none of the learning algorithms showed strong advantages over any other.

This paper summarizes our previous works in modeling non-verbal behaviors for natural human-robot interaction (HRI) and discusses a path for integrating them into spoken dialogs. While some non-verbal behaviors can be considered “optional” elements to be added to a spoken dialog, some non-verbal behaviors substantially require a harmonized plan that simultaneously considers both spoken dialog and non-verbal behavior. The paper discusses such unique HRI features.

The development of hand-crafted action and dialog generation models for a social robot is a time consuming process that yields a solution only for the relatively narrow range of interactions envisioned by the programmers. In this paper, we propose a data-driven solution for interactive behavior generation that leverages online games as a means of collecting large-scale data corpora for human-robot interaction research. We present a system in which action and dialog models for a collaborative human-robot task are learned based on a reproduction of the task in a two-player online game called Mars Escape.

Cochairs and Program Committee for the AAAI 2010 Symposium on Dialog with Robots.

The aim of this paper is to revisit the fundamental requirements for bulding computational models for Interactive Narrative. We express the need for broader computational models of narrative and underline the fundamental difference between models for story generation and models for Interactive Narrative. Research directions are finally sketched to move towards dedicated computational models for Interactive Narrative.

Computational Narrative has provided several examples of how to process narrations using semantical approaches. While many useful concepts for computational management of stories have been unveiled, a common barrier has hindered their development: semantic knowledge is still too complex to handle. In this paper, a focus shift based on narrative structure is proposed. Instead of digging deeper into the possibilities of semantic processing, analysing structural properties of stories and keeping the semantic load to a minimum can allow for a more efficient use of available narrative corpora, even without mimicking human behaviour.

Links between licit and illicit economies fuel conflict in countries mired in irregular warfare. We argue that in Afghanistan, cultivating poppy and trading drugs bring stability to farmers who face the unintended consequences of haphazard development efforts while lacking alternative livelihoods and security necessary to access markets. Drug trafficking funds the crime-insurgency nexus and government corruption, in turn foiling attempts to establish a unified governance body. We show how individual rationality, market forces, corruption and opium stocks accumulated at different stages in the supply chain counteract the effects of poppy eradication. To that end, we use initial results from a multiagent model of the Afghan drug industry. We define physical, administrative, social and infrastructural environments in the simulation, and outline objectives and inputs for decision making and the structure of actor interactions.