We consider the problem of modifying a description logic concept in light of models represented as pointed interpretations. We call this setting model change, and distinguish three main kinds of changes: eviction, which consists of only removing models; reception, which incorporates models; and revision, which combines removal with incorporation of models in a single operation. We introduce a formal notion of revision and argue that it does not reduce to a simple combination of eviction and reception, contrary to intuition. We provide positive and negative results on the compatibility of eviction and reception for EL-bottom and ALC description logic concepts and on the compatibility of revision for ALC concepts.

While pressing, this narrow focus overlooks critical human-centric considerations that shape the long-term trajectory of a society. In this position paper, we identify the risks of overlooking the impact of AI on the future of work and recommend comprehensive transition support towards the evolution of meaningful labor with human agency. Through the lens of economic theories, we highlight the intertemporal impacts of AI on human livelihood and the structural changes in labor markets that exacerbate income inequality. Additionally, the closed-source approach of major stakeholders in AI development resembles rent-seeking behavior through exploiting resources, breeding mediocrity in creative labor, and monopolizing innovation.

The IJCAI-2021 awards were announced during the opening ceremony of the International Joint Conference on Artificial Intelligence (IJCAI-21). The honours included the 2021 AIJ classic paper award, the AIJ prominent paper award, and the IJCAI-JAIR best paper prize. This award recognizes outstanding papers, exceptional in their significance and impact, that were published at least 15 years ago, in the journal Artificial Intelligence (AIJ). This paper brought partially observable Markov decision processes (POMDPs) from the field of operational research to the field of AI. It provides an excellent account of the theory behind POMDPs, which demystified the field for a generation of researchers, and popularised their use in both AI and robotics.

International Conference on Learning Representations (ICLR) recently announced the ICLR 2021 Outstanding Paper Awards winners. It recognised eight papers out of the 860 submitted this year. The papers were evaluated for both technical quality and the potential to create a practical impact. The committee was chaired by Ivan Titov (U. This paper deals with parameterising hypercomplex multiplications using arbitrarily learnable parameters compared with the fully-connected layer counterpart.