Titlebook: Artificial Intelligence XXXVIII; 41st SGAI Internatio Max Bramer,Richard Ellis Conference proceedings 2021 Springer Nature Switzerland AG 2

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Probabilistic Rule Induction for Transparent CBR Under Uncertainty We show how probabilistic inductive logic programming (PILP) can be applied in CBR systems to make transparent decisions combining logic and probabilities. Then, we demonstrate how our approach can be applied in scenarios presenting uncertainty.

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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/b/image/162164.jpg

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https://doi.org/10.1007/978-3-030-58271-5representations including arbitrarily complex relationships between entities such as human interactions. This is particularly interesting in the context of social navigation, where relational information should be considered. This paper presents a model combining Graph Neural Network (GNN) and Convo

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Counter-mapping platform urbanism, can be used to simulate spiking neural networks, and the standard learning rule is based on the timing of the spikes of the pre and post-synaptic neurons. This paper describes the use of these models to categorise documents by translating this Spike Timing Dependent Plasticity into an unsupervised

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Impact of platforms on urban space,. In the case of model-free learning, the algorithm learns through trial and error in the target environment in contrast to model-based where the agent train in a learned or known environment instead..Model-free reinforcement learning shows promising results in simulated environments but falls short

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Sanja Kutnjak Ivkovi?,M. R. Haberfeldno justification for generated solutions and these solutions are non-trivial to analyse in most cases. We propose that identifying the combinations of variables that strongly influence solution quality, and the nature of this relationship, represents a step towards explaining the choices made by a m

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Sanja Kutnjak Ivkovi?,M. R. Haberfeldnclusions. In multi-agent settings, where several agents can advance arguments at the same time, understanding which agent has the most influence on a particular argument can improve an agent’s decision about which argument to advance next. In this paper, we introduce an argumentation framework with

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