The ability to refer to entities such as objects, locations, and people is an important capability for robots designed to interact with humans. For example, a referring expression (RE) such as “Do you mean the box on the left?” might be used by a robot seeking to disambiguate between objects. In this paper, we present and evaluate algorithms for Referring Expression Generation (REG) in small-scale situated contexts. We first present data regarding how humans generate small-scale spatial referring expressions (REs). We then use this data to define five categories of observed small-scale spatial REs, and use these categories to create an ensemble of REG algorithms. Next, we evaluate REs generated by those algorithms and by humans both subjectively (by having participants rank REs), and objectively, (by assessing task performance when participants use REs) through a set of interrelated crowdsourced experiments. While our machine generated REs were subjectively rated lower than those generated by humans, they objectively significantly outperformed human REs. Finally, we discuss the main contributions of this work: (1) a dataset of images and REs, (2) a categorization of observed small-scale spatial REs, (3) an ensemble of REG algorithms, and (4) a crowdsourcing-based framework for subjectively and objectively evaluating REG.

Reports on the 2016 AAAI Fall Symposium Series

The AAAI 2016 Fall Symposium Series was held Thursday through Saturday, November 17–19, at the Westin Arlington Gateway in Arlington, Virginia adjacent to Washington, DC. The titles of the six symposia were Accelerating Science: A Grand Challenge for AI; Artificial Intelligence for Human-Robot Interaction, Cognitive Assistance in Government and Public Sector Applications, Cross-Disciplinary Challenges for Autonomous Systems, Privacy and Language Technologies, Shared Autonomy in Research and Practice. The highlights of each (except Acceleration Science) symposium are presented in this report.

The 7th Symposium on Educational Advances in Artificial Intelligence (EAAI'17, co-chaired by Sven Koenig and Eric Eaton) launched the EAAI New and Future AI Educator Program to support the training of early-career university faculty, secondary school faculty, and future educators (PhD candidates or postdocs who intend a career in academia). As part of the program, awardees were asked to address one of the following "blue sky" questions: * How could/should Artificial Intelligence (AI) courses incorporate ethics into the curriculum? * How could we teach AI topics at an early undergraduate or a secondary school level? * AI has the potential for broad impact to numerous disciplines. How could we make AI education more interdisciplinary, specifically to benefit non-engineering fields? This paper is a collection of their responses, intended to help motivate discussion around these issues in AI education.

A major challenge for robots interacting with humans in realistic environments is handling robots' uncertainty with respect to the identities and properties of the people, places, and things found in their environments: a problem compounded when humans refer to these entities using underspecified language. In this paper, we present a framework for generating clarification requests in the face of both pragmatic and referential ambiguity, and show how we are able to handle several stages of this framework by integrating a Dempster-Shafer (DS)-theoretic pragmatic reasoning component with a probabilistic reference resolution component.

We present a domain-independent approach to reference resolution that allows a robotic or virtual agent to resolve references to entities (e.g., objects and locations) found in open worlds when the information needed to resolve such references is distributed among multiple heterogeneous knowledge bases in its architecture. An agent using this approach can combine information from multiple sources without the computational bottleneck associated with centralized knowledge bases. The proposed approach also facilitates “lazy constraint evaluation”, i.e., verifying properties of the referent through different modalities only when the information is needed. After specifying the interfaces by which a reference resolution algorithm can request information from distributed knowledge bases, we present an algorithm for performing open-world reference resolution within that framework, analyze the algorithm’s performance, and demonstrate its behavior on a simulated robot.

Chai et al. present a greedy As natural language capable robots and other agents become algorithm which uses a subset of the Givenness Hierarchy more commonplace, the ability for these agents to understand to resolve a wide array of referential expressions, but this truly natural human speech is becoming increasingly approach operates under a closed-world assumption (Chai, important. What is more, these agents must be able to understand Prasov, and Qu 2006). Kollar, Tellex et al. present Generalized truly natural human speech in realistic scenarios, Grounding Graphs, which instantiate probabilistic in which an agent may not have full certainty in its knowledge graphical models based on the structure of incoming NL utterances, of its environment, and in which an agent may not have and use those models to resolve references (Tellex full knowledge of the entities contained in its environment.

Natural language dialogue provides the opportunity fortruly natural human-robot interaction. A robot participating in natural language dialogue must identify or create new representations for referenced entities if it is to discuss, reason about, or perform actions involving that entity, a capability known as reference resolution. In previous work we presented algorithms for resolving references occurring in definite noun phrases. In this paper we propose an algorithm for resolving references in a wider array of linguistic forms, using the Givenness Hierarchy.

The ultimate goal of human natural language interaction is to communicate intentions. However, these intentions are often not directly derivable from the semantics of an utterance (e.g., when linguistic modulations are employed to convey polite-ness, respect, and social standing). Robotic architectures withsimple command-based natural language capabilities are thus not equipped to handle more liberal, yet natural uses of linguistic communicative exchanges. In this paper, we propose novel mechanisms for inferring in-tentions from utterances and generating clarification requests that will allow robots to cope with a much wider range of task-based natural language interactions. We demonstrate the potential of these inference algorithms for natural human-robot interactions by running them as part of an integrated cognitive robotic architecture on a mobile robot in a dialogue-based instruction task.