Author name cluster

Gordon Briggs

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5 papers

2 author rows

AAAI Conference 2015 Conference Paper

Going Beyond Literal Command-Based Instructions: Extending Robotic Natural Language Interaction Capabilities

Tom Williams
Gordon Briggs
Bradley Oosterveld
Matthias Scheutz

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 politeness, respect, and social standing). Robotic architectures with simple 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 intentions 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 humanrobot interactions by running them as part of an integrated cognitive robotic architecture on a mobile robot in a dialoguebased instruction task.

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IROS Conference 2015 Conference Paper

Planning for serendipity

Tathagata Chakraborti
Gordon Briggs
Kartik Talamadupula
Yu Zhang 0055
Matthias Scheutz
David E. Smith 0001
Subbarao Kambhampati

Recently there has been a lot of focus on human robot co-habitation issues that are often orthogonal to many aspects of human-robot teaming; e. g. on producing socially acceptable behaviors of robots and de-conflicting plans of robots and humans in shared environments. However, an interesting offshoot of these settings that has largely been overlooked is the problem of planning for serendipity - i. e. planning for stigmergic collaboration without explicit commitments on agents in co-habitation. In this paper we formalize this notion of planning for serendipity for the first time, and provide an Integer Programming based solution for this problem. Further, we illustrate the different modes of this planning technique on a typical Urban Search and Rescue scenario and show a real-life implementation of the ideas on the Nao Robot interacting with a human colleague.

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IROS Conference 2014 Conference Paper

Coordination in human-robot teams using mental modeling and plan recognition

Kartik Talamadupula
Gordon Briggs
Tathagata Chakraborti
Matthias Scheutz
Subbarao Kambhampati

Beliefs play an important role in human-robot teaming scenarios, where the robots must reason about other agents' intentions and beliefs in order to inform their own plan generation process, and to successfully coordinate plans with the other agents. In this paper, we cast the evolving and complex structure of beliefs, and inference over them, as a planning and plan recognition problem. We use agent beliefs and intentions modeled in terms of predicates in order to create an automated planning problem instance, which is then used along with a known and complete domain model in order to predict the plan of the agent whose beliefs are being modeled. Information extracted from this predicted plan is used to inform the planning process of the modeling agent, to enable coordination. We also look at an extension of this problem to a plan recognition problem. We conclude by presenting an evaluation of our technique through a case study implemented on a real robot.

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AAAI Conference 2013 Conference Paper

A Hybrid Architectural Approach to Understanding and Appropriately Generating Indirect Speech Acts

Gordon Briggs
Matthias Scheutz

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.

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AAAI Conference 2013 Conference Paper

Grounding Natural Language References to Unvisited and Hypothetical Locations

Thomas Williams
Rehj Cantrell
Gordon Briggs
Paul Schermerhorn
Matthias Scheutz

While much research exists on resolving spatial natural language references to known locations, little work deals with handling references to unknown locations. In this paper we introduce and evaluate algorithms integrated into a cognitive architecture which allow an agent to learn about its environment while resolving references to both known and unknown locations. We also describe how multiple components in the architecture jointly facilitate these capabilities.

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