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This paper provides an overview of an architecture based on the principle of step-wise refinement for knowledge representation, explainable reasoning, and interactive learning, in robotics. We briefly describe the individual components of the architecture, and emphasize how the interplay between representation, reasoning, and learning, can be exploited to enable reliable and efficient decision making in dynamic domains

ECAI 2020

REBA-KRL: Refinement-based Architecture for Knowledge Representation, Explainable Reasoning, and Interactive Learning in Robotics

step-wise refinement

explainable reasoning and learning

human-robot collaboration

interactive learning

knowledge representation

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ECAI2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI, including:




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… and many more!

ECAI 2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI.

The problem of reasoning with context dependent knowledge has recently gained interest in the area of description logic based knowledge bases (KBs). Among the several proposals, we consider the Contextualized Knowledge Repository (CKR) framework. The CKR model has been recently extended with the capability of reasoning with global (context independent) defeasible axioms that can be overridden by local (context specific) knowledge. In CKR applications it is often useful to reason over a hierarchical organization of contexts. We highlight here our recent efforts on extending the CKR framework to allow for the representation of exception handling in the inheritance of knowledge across local contexts. We first concentrate on a limitation to a particular kind of context organization, i.e. ranked hierarchies, which allows us to simplify the definition of reasoning procedures.We then further generalize the proposal to extend the reasoning on exception handling over general contextual hierarchies.

Reasoning with Justifiable Exceptions in Contextual Hierarchies

Leo-III is an effective automated theorem prover for extensional type theory with Henkin semantics. It is based on an extensional higher-order paramodulation calculus and supports reasoning in monomorphic and rank-1 polymorphic first-order and higher-order logics. Leo-III also automates various non-classical logics, including almost every normal higher-order modal logic.

The Higher-Order Prover Leo-III

Actual Causality Canvas: A General Framework for Explanation-based Socio-Technical Constructs

Entropy-based adaptive exploit-explore coefficient for Monte-Carlo path planning

"Semantic interpretation of dynamic visuospatial imagery calls for a general and systematic integration of methods in knowledge representation and computer vision. Towards this, we highlight research articulating and developing deep (visuospatial) semantics, characterised by the existence of declarative models –e.g., pertaining space and motion– and corresponding formalisation and reasoning methods supporting capabilities such as semantic question-answering, relational visuospatial learning, and (non-monotonic) visuospatial explanation. We position a working model for deep semantics by highlighting our select recent works from IJCAI 2019, AAAI 2018, and ILP 2016 & ACS 2018. 

We posit that human-centred, explainable visual sensemaking necessitates both high-level semantics and low-level visual computing, with the highlighted works providing a model for systematic, modular integration of diverse multifaceted techniques and perspectives from AI, ML, Vision, Visual Perception, and Spatial Cognition."

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Reasoning with Justifiable Exceptions in Contextual Hierarchies

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