Handling Semantic Inconsistencies in Commonsense Knowledge for Autonomous Service Robots

Jakob, Stefan; Opfer, Stephan; Jahl, Alexander; Baraki, Harun; Geihs, Kurt

doi:10.1109/icsc.2020.00026

Cited by 4 publications

(19 citation statements)

References 10 publications

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“…The usage of commonsense knowledge shown by Lemaignan et al is similar to our usage of commonsense knowledge presented in [104,107] and our detection of inconsistencies introduced in [78]. Both enrich their knowledge bases with commonsense knowledge extracted from external sources.…”

Section: Lemaignan Et Al Present Inmentioning

confidence: 56%

“…In contrast, the full set of over 34 base relations is applied in [104,107]. To prevent semantic inconsistencies in the knowledge base of a robot, we introduce an automatic prevention of inconsistencies in [78], which relies on commonsense knowledge to find contradictions in the properties of an object. [86] a framework for autonomous robots with advanced human-robot interaction skills.…”

Section: Application Of Commonsense Knowledgementioning

confidence: 99%

“…Lemaignan et al rely on OWL, which requires the reclassification of the complete knowledge base to introduce additional classes. This disadvantage can be removed using ASP as proposed by us in [78,104,107]. [6] a system for ambient assisted living, which applies commonsense knowledge and reasoning.…”

Section: Lemaignan Et Al Present Inmentioning

confidence: 99%

“…The framework introduced by Ayari et al is similar to the combination of our modelling of commonsense knowledge [107] and our inconsistency handling [78]. Both approaches use commonsense knowledge to support agents in solving their tasks, for example, in a household scenario.…”

Section: Ayari Et Al Present Inmentioning

confidence: 99%

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A Self-Organising Multi-Agent Knowledge Base

Jakob

Jahl

Baraki

et al. 2020

2020 IEEE International Conference on Web Services (ICWS)

Self Cite

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1 bird(tweety). 2 bird(tux). 3 penguin(tux). 4 flies(X) :-bird(X), not -flies(X). 5 -flies(X) :-bird(X), penguin(X). 6 :-flies(X), -flies(X). Listing 2.1: Example ASP ProgramAdditionally, ASP supports formulating disjunctions in the rule heads. An example of a disjunctive rule is given in Line 3 in Listing 2.2. This rule states that a bird can either fly or not fly.1 bird(tweety). 2 bird(tux). 3 -flies(X), flies(X) :-bird(X). Listing 2.2: Disjunctive ASP Program1 bird(tweety). 2 bird(tux). 3 penguin(tux). 4 flies(tweety) :-bird(tweety), not -flies(tweety). 5 flies(tux) :-bird(tux), not -flies(tux). 6 -flies(tux) :-bird(tux), penguin(tux). 7 :-flies(tweety), -flies(tweety). 8 :-flies(tux), -flies(tux).

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Section: Lemaignan Et Al Present Inmentioning

confidence: 56%

Section: Application Of Commonsense Knowledgementioning

confidence: 99%

Section: Lemaignan Et Al Present Inmentioning

confidence: 99%

Section: Ayari Et Al Present Inmentioning

confidence: 99%

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A Self-Organising Multi-Agent Knowledge Base

Jakob

Jahl

Baraki

et al. 2020

2020 IEEE International Conference on Web Services (ICWS)

Self Cite

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“…KnowRob has multiple knowledge representation and reasoning capabilities and has been successfully deployed in complex tasks, such as identifying missing items on a table [38], operating containers [39], multi-robot coordination [40], and semantic mapping [41]. Non-monotonic knowledge representation and reasoning systems are typically based on Answer Set Programming (ASP) (e.g., References [29,42,43]) and extensions of OWL-DL that allow the use of incomplete information have been defined (e.g., References [44]), some of which have been demonstrated in different complex tasks [42,[44][45][46][47][48]. Awaad et al [44] use OWL-DL to model preferences and functional affordances for establishing social-accepted behaviors and guidelines to improve human-robot interaction and carrying out tasks in real-world scenarios.…”

Section: Related Workmentioning

confidence: 99%

Deliberative and Conceptual Inference in Service Robots

et al. 2021

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Service robots need to reason to support people in daily life situations. Reasoning is an expensive resource that should be used on demand whenever the expectations of the robot do not match the situation of the world and the execution of the task is broken down; in such scenarios, the robot must perform the common sense daily life inference cycle consisting on diagnosing what happened, deciding what to do about it, and inducing and executing a plan, recurring in such behavior until the service task can be resumed. Here, we examine two strategies to implement this cycle: (1) a pipe-line strategy involving abduction, decision-making, and planning, which we call deliberative inference and (2) the use of the knowledge and preferences stored in the robot’s knowledge-base, which we call conceptual inference. The former involves an explicit definition of a problem-space that is explored through heuristic search, and the latter is based on conceptual knowledge, including the human user preferences, and its representation requires a non-monotonic knowledge-based system. We compare the strengths and limitations of both approaches. We also describe a service robot conceptual model and architecture capable of supporting the daily life inference cycle during the execution of a robotics service task. The model is centered in the declarative specification and interpretation of robot’s communication and task structure. We also show the implementation of this framework in the fully autonomous robot Golem-III. The framework is illustrated with two demonstration scenarios.

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Commonsense knowledge in cognitive robotics: a systematic literature review

Töberg,

Ngonga Ngomo,

Beetz

et al. 2024

Front. Robot. AI

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One of the big challenges in robotics is the generalization necessary for performing unknown tasks in unknown environments on unknown objects. For us humans, this challenge is simplified by the commonsense knowledge we can access. For cognitive robotics, representing and acquiring commonsense knowledge is a relevant problem, so we perform a systematic literature review to investigate the current state of commonsense knowledge exploitation in cognitive robotics. For this review, we combine a keyword search on six search engines with a snowballing search on six related reviews, resulting in 2,048 distinct publications. After applying pre-defined inclusion and exclusion criteria, we analyse the remaining 52 publications. Our focus lies on the use cases and domains for which commonsense knowledge is employed, the commonsense aspects that are considered, the datasets/resources used as sources for commonsense knowledge and the methods for evaluating these approaches. Additionally, we discovered a divide in terminology between research from the knowledge representation and reasoning and the cognitive robotics community. This divide is investigated by looking at the extensive review performed by Zech et al. (The International Journal of Robotics Research, 2019, 38, 518–562), with whom we have no overlapping publications despite the similar goals.

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Handling Semantic Inconsistencies in Commonsense Knowledge for Autonomous Service Robots

Cited by 4 publications

References 10 publications

A Self-Organising Multi-Agent Knowledge Base

A Self-Organising Multi-Agent Knowledge Base

Deliberative and Conceptual Inference in Service Robots

Commonsense knowledge in cognitive robotics: a systematic literature review

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