If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
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.
Alves-Oliveira, Patrícia (Instituto Universitário de Lisboa) | Freedman, Richard G. (University of Massachusetts Amherst) | Grollman, Dan (Sphero, Inc.) | Herlant, Laura (arnegie Mellon University) | Humphrey, Laura (Air Force Research Laboratory) | Liu, Fei (University of Central Florida) | Mead, Ross (Semio) | Stein, Frank (IBM) | Williams, Tom (Tufts University) | Wilson, Shomir (University of Cincinnati)
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.
Williams, Tom (Tufts University)
As natural language capable robots and other agents become more commonplace, the ability for these agents to understand truly natural human speech is becoming increasingly important. What is more, these agents must be able to understand truly natural human speech in realistic scenarios, in which an agent may not have full certainty in its knowledge of its environment, and in which an agent may not have full knowledge of the entities contained in its environment. As such, I am interested in developing architectural mechanisms which will allow robots to understand natural language in uncertain and open-worlds. My work towards this goal has primarily focused on two problems: (1) reference resolution, and (2) pragmatic reasoning.
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.
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.