Correct High-level Robot Behavior in Environments with Unexpected Events

Wong, Kai Weng; Ehlers, Rüdiger; Kress-Gazit, Hadas

doi:10.15607/rss.2014.x.012

Cited by 21 publications

(4 citation statements)

References 26 publications

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“…For that reason, verifiability is not presently a primary concern underlying DI-HDCA modeling, but as the verifiable correctness of complex computational agents becomes more important, it may become more beneficial to have models of intelligent robots grounded in a framework that enables verification. Moreover, there are promising HDS-related approaches to creating verifiably correct behaviors, such as synthesizing robot controllers from formal specifications [e.g., Wong et al (2014)]; the gap between such approaches and DI-HDCA modeling is sizable, but less than the gap between such approaches and models without formal foundations.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Dynamical Intention: Integrated Intelligence Modeling for Goal-Directed Embodied Agents

Aaron

2016

Front. Robot. AI

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Intelligent embodied robots are integrated systems: as they move continuously through their environments, executing behaviors and carrying out tasks, components for low-level and high-level intelligence are integrated in the robot's cognitive system, and cognitive and physical processes combine to create their behavior. For a modeling framework to enable the design and analysis of such integrated intelligence, the underlying representations in the design of the robot should be dynamically sensitive, capable of reflecting both continuous motion and micro-cognitive influences, while also directly representing the necessary beliefs and intentions for goal-directed behavior. In this paper, a dynamical intention-based modeling framework is presented that satisfies these criteria, along with a hybrid dynamical cognitive agent (HDCA) framework for employing dynamical intentions in embodied agents. This dynamical intention-HDCA (DI-HDCA) modeling framework is a fusion of concepts from spreading activation networks, hybrid dynamical system models, and the BDI (belief-desire-intention) theory of goal-directed reasoning, adapted and employed unconventionally to meet entailments of environment and embodiment. The paper presents two kinds of autonomous agent learning results that demonstrate dynamical intentions and the multi-faceted integration they enable in embodied robots: with a simulated service robot in a grid-world office environment, reactive-level learning minimizes reliance on deliberative-level intelligence, enabling task sequencing and action selection to be distributed over both deliberative and reactive levels; and with a simulated game of Tag, the cognitive-physical integration of an autonomous agent enables the straightforward learning of a user-specified strategy during gameplay, without interruption to the game. In addition, the paper argues that dynamical intentions are consistent with cognitive theory underlying goal-directed behavior, and that DI-HDCA modeling may facilitate the study of emergent behaviors in embodied agents.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Dynamical Intention: Integrated Intelligence Modeling for Goal-Directed Embodied Agents

Aaron

2016

Front. Robot. AI

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“…feedback is provided to the user, who is then asked to resolve the conflict by modifying the specification [46,47].…”

Section: Monitoring the Implementationmentioning

confidence: 99%

The Challenges in Specifying and Explaining Synthesized Implementations of Reactive Systems

Kress-Gazit

Torfah

2019

Electron. Proc. Theor. Comput. Sci.

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In formal synthesis of reactive systems an implementation of a system is automatically constructed from its formal specification. The great advantage of synthesis is that the resulting implementation is correct by construction; therefore there is no need for manual programming and tedious debugging tasks. Developers remain, nevertheless, hesitant to using automatic synthesis tools and still favor manually writing code. A common argument against synthesis is that the resulting implementation does not always give a clear picture on what decisions were made during the synthesis process. The outcome of synthesis tools is mostly unreadable and hinders the developer from understanding the functionality of the resulting implementation. Many attempts have been made in the last years to make the synthesis process more transparent to users. Either by structuring the outcome of synthesis tools or by providing additional automated support to help users with the specification process.In this paper we discuss the challenges in writing specifications for reactive systems and give a survey on what tools have been developed to guide users in specifying reactive systems and understanding the outcome of synthesis tools.

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“…Robot behavior can also be repaired by dynamic synthesis of new control structures and through program synthesis [29], such as automatic synthesis of new FSMs [30], synthesis of code from a context-free motion grammar with parameters derived from human-inspired control [31]. Programming by example synthesizes programs from a small number of examples [32] and can also support noisy data [33].…”

Section: Behavior Synthesismentioning

confidence: 99%

“…Each trial applies SRTR to a subset of the corrections, solves the resulting formulation with both Z3 and dReal, and evaluates the performance on the test dataset. We repeat this procedure 30 times for each number of corrections N ∈ [1,30], selecting N random corrections at each iteration, for a total of 900, 000 total trials.…”

Section: Performancementioning

confidence: 99%

Interactive Robot Transition Repair With SMT

Holtz

Guha

Biswas

2018

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence

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Complex robot behaviors are often structured as state machines, where states encapsulate actions and a transition function switches between states. Since transitions depend on physical parameters, when the environment changes, a roboticist has to painstakingly readjust the parameters to work in the new environment. We present interactive SMTbased Robot Transition Repair (SRTR): instead of manually adjusting parameters, we ask the roboticist to identify a few instances where the robot is in a wrong state and what the right state should be. An automated analysis of the transition function 1) identifies adjustable parameters, 2) converts the transition function into a system of logical constraints, and 3) formulates the constraints and user-supplied corrections as a MaxSMT problem that yields new parameter values. We show that SRTR finds new parameters 1) quickly, 2) with few corrections, and 3) that the parameters generalize to new scenarios. We also show that a SRTR-corrected state machine can outperform a more complex, expert-tuned state machine.

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Correct High-level Robot Behavior in Environments with Unexpected Events

Cited by 21 publications

References 26 publications

Dynamical Intention: Integrated Intelligence Modeling for Goal-Directed Embodied Agents

Dynamical Intention: Integrated Intelligence Modeling for Goal-Directed Embodied Agents

The Challenges in Specifying and Explaining Synthesized Implementations of Reactive Systems

Interactive Robot Transition Repair With SMT

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