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Evan Krause

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6 papers

1 author row

AIJ Journal 2024 Journal Article

A neurosymbolic cognitive architecture framework for handling novelties in open worlds

Shivam Goel
Panagiotis Lymperopoulos
Ravenna Thielstrom
Evan Krause
Patrick Feeney
Pierrick Lorang
Sarah Schneider
Yichen Wei

“Open world” environments are those in which novel objects, agents, events, and more can appear and contradict previous understandings of the environment. This runs counter to the “closed world” assumption used in most AI research, where the environment is assumed to be fully understood and unchanging. The types of environments AI agents can be deployed in are limited by the inability to handle the novelties that occur in open world environments. This paper presents a novel cognitive architecture framework to handle open-world novelties. This framework combines symbolic planning, counterfactual reasoning, reinforcement learning, and deep computer vision to detect and accommodate novelties. We introduce general algorithms for exploring open worlds using inference and machine learning methodologies to facilitate novelty accommodation. The ability to detect and accommodate novelties allows agents built on this framework to successfully complete tasks despite a variety of novel changes to the world. Both the framework components and the entire system are evaluated in Minecraft-like simulated environments. Our results indicate that agents are able to efficiently complete tasks while accommodating “concealed novelties” not shared with the architecture development team.

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IJCAI Conference 2018 Conference Paper

Recursive Spoken Instruction-Based One-Shot Object and Action Learning

Matthias Scheutz
Evan Krause
Bradley Oosterveld
Tyler Frasca
Robert Platt

Learning new knowledge from single instructions and being able to apply it immediately is highly desirable for artificial agents. We provide the first demonstration of spoken instruction-based one-shot object and action learning in a cognitive robotic architecture and briefly discuss the architectural modifications required to enable such fast learning, demonstrating the new capabilities on a fully autonomous robot.

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AAMAS Conference 2017 Conference Paper

Spoken Instruction-Based One-Shot Object and Action Learning in a Cognitive Robotic Architecture

Matthias Scheutz
Evan Krause
Brad Oosterveld
Tyler Frasca
Robert Platt

Learning new knowledge from single instructions and being able to apply it immediately is a highly desirable capability for artificial agents. We provide the first demonstration of spoken instructionbased one-shot object and action learning in a cognitive robotic architecture and discuss the modifications to several architectural components required to enable such fast learning, demonstrating the new capabilities on two different fully autonomous robots. CCS Concepts •Human-centered computing → Natural language interfaces; •Computing methodologies → Online learning settings;

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AAMAS Conference 2016 Conference Paper

Analogical Generalization of Actions from Single Exemplars in a Robotic Architecture

Jason R. Wilson
Evan Krause
Matthias Scheutz
Morgan Rivers

Humans are often able to generalize knowledge learned from a single exemplar. In this paper, we present a novel integration of mental simulation and analogical generalization algorithms into a cognitive robotic architecture that enables a similarly rudimentary generalization capability in robots. Specifically, we show how a robot can generate variations of a given scenario and then use the results of those new scenarios run in a physics simulator to generate generalized action scripts using analogical mappings. The generalized action scripts then allow the robot to perform the originally learned activity in a wider range of scenarios with different types of objects without the need for additional exploration or practice. In a proof-of-concept demonstration we show how the robot can generalize from a previously learned pickand-place action performed with a single arm on an object with a handle to a pick-and-place action of a cylindrical object with no handle with two arms.

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AAAI Conference 2014 Conference Paper

Learning to Recognize Novel Objects in One Shot through Human-Robot Interactions in Natural Language Dialogues

Evan Krause
Michael Zillich
Thomas Williams
Matthias Scheutz

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.

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AAAI Conference 2012 Conference Paper

Crossing Boundaries: Multi-Level Introspection in a Complex Robotic Architecture for Automatic Performance Improvements

Evan Krause
Paul Schermerhorn
Matthias Scheutz

Introspection mechanisms are employed in agent architectures to improve agent performance. However, there is currently no approach to introspection that makes automatic adjustments at multiple levels in the implemented agent system. We introduce our novel multi-level introspection framework that can be used to automatically adjust architectural configurations based on the introspection results at the agent, infrastructure and component level. We demonstrate the utility of such adjustments in a concrete implementation on a robot where the high-level goal of the robot is used to automatically configure the vision system in a way that minimizes resource consumption while improving overall task performance.

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