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.

Much existing work examining the ethical behaviors of robots does not consider the impact and effects of long- term human-robot interactions. A robot teammate, col- laborator or helper is often expected to increase task performance, individually or of the team, but little dis- cussion is usually devoted to how such a robot should balance the task requirements with building and main- taining a “working relationship” with a human partner, much less appropriate social relations outside that team. We propose the “Relational Enhancement” framework for the design and evaluation of long-term interactions, which composed of interrelated concepts of efficiency, solidarity, and prosocial concern. We discuss how this framework can be used to evaluate common existing ap- proaches in cognitive architectures for robots and then examine how social norms and mental simulation may contribute to each of the components of the framework.

Certain stimuli in the environment reliably, and perhaps inevitably, trigger human cognitive and behavioral responses. We suggest that the presence of such “trigger stimuli” in modern robots can have disconcerting consequences. We provide one new example of such consequences: a reversal of a pattern of moral judgments people make about robots, depending on whether they view a “mechanical” or a “humanoid” robot.

An ongoing goal at the intersection of artificial intelligence In this paper, we briefly present initial work that has (AI), robotics, and human-robot interaction (HRI) is to create been done in the DIARC/ADE cognitive robotic architecture autonomous agents that can assist and interact with human (Schermerhorn et al. 2006; Kramer and Scheutz 2006) to enable teammates in natural and humanlike ways. This is a such a rejection and explanation mechanism. First we multifaceted challenge, involving both the development of discuss the theoretical considerations behind this challenge, an ever-expanding set of capabilities (both physical and algorithmic) specifically the conditions that must be met for a directive to such that robotic agents can autonomously engage be appropriately accepted. Next, we briefly present some of in a variety of useful tasks, as well as the development the explicit reasoning mechanisms developed in order to facilitate of interaction mechanisms (e.g.

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.

Being able to quickly and naturally teach robots new knowledge is critical for many future open-world human-robot interaction scenarios. In this paper we present a novel approach to using natural language context for one-shot learning of visual objects, where the robot is immediately able to recognize the described object. We describe the architectural components and demonstrate the proposed approach on a robotic platform in a proof-of-concept evaluation.

Current approaches to handling indirect speech acts (ISAs) do not account for their sociolinguistic underpinnings (i.e., politeness strategies). Deeper understanding and appropriate generation of indirect acts will require mechanisms that integrate natural language (NL) understanding and generation with social information about agent roles and obligations,which we introduce in this paper. Additionally, we tackle the problem of understanding and handling indirect answers that take the form of either speech acts or physical actions, which requires an inferential, plan-reasoning approach. In order to enable artificial agents to handle an even wider-variety of ISAs, we present a hybrid approach, utilizing both the idiomatic and inferential strategies. We then demonstrate our system successfully generating indirect requests and handling indirect answers, and discuss avenues of future research.

Natural human-like human-robot interactions require many functional capabilities from a robot that have to be reflected in architectural components in the robotic control architecture.  In particular, various mechanisms for producing social behaviors , goal-oriented cognition , and robust intelligence are required.  In this paper, we present an overview of the most recent version of our DIARC architecture and show how several novel algorithms attempt to address these three areas, leading to more natural interactions with humans, while also extending the overall capability of the integrated system.

Introspection mechanisms are employed in agent architectures toimprove agent performance. However, there is currently no approach tointrospection that makes automatic adjustments at multiple levels inthe implemented agent system. We introduce our novel multi-levelintrospection framework that can be used to automatically adjustarchitectural configurations based on the introspection results at theagent, infrastructure and component level. We demonstrate the utilityof such adjustments in a concrete implementation on a robot where thehigh-level goal of the robot is used to automatically configure thevision system in a way that minimizes resource consumption whileimproving overall task performance.