Artificial Intelligence (cs.AI)

Abstract: Large-scale pre-trained models have been known that they are transferable, and they generalize well on the unseen dataset. Recently, multimodal pre-trained models such as CLIP show significant performance improvement in diverse experiments. However, when the labeled dataset is limited, the generalization of a new dataset or domain is still challenging. To improve the generalization performance on few-shot learning, there have been diverse efforts, such as prompt learning and adapter. However, the current few-shot adaptation methods are not interpretable, and they require a high computation cost for adaptation. In this study, we propose a new method, robust prompt learning with knowledge graph (RPLKG). Based on the knowledge graph, we automatically design diverse interpretable and meaningful prompt sets. Our model obtains cached embeddings of prompt sets after one forwarding from a large pre-trained model. After that, model optimizes the prompt selection processes with GumbelSoftmax. In this way, our model is trained using relatively little memory and learning time. Also, RPLKG selects the optimal interpretable prompt automatically, depending on the dataset. In summary, RPLKG is i) interpretable, ii) requires small computation resources, and iii) easy to incorporate prior human knowledge. To validate the RPLKG, we provide comprehensive experimental results on few-shot learning, domain generalization and new class generalization setting. RPLKG shows a significant performance improvement compared to zero-shot learning and competitive performance against several prompt learning methods using much lower resources.

Abstract: Balancing exploration and conservatism in the constrained setting is an important problem if we are to use reinforcement learning for meaningful tasks in the real world. In this paper, we propose a principled algorithm for safe exploration based on the concept of shielding. Previous approaches to shielding assume access to a safety-relevant abstraction of the environment or a high-fidelity simulator. Instead, our work is based on latent shielding - another approach that leverages world models to verify policy roll-outs in the latent space of a learned dynamics model. Our novel algorithm builds on this previous work, using safety critics and other additional features to improve the stability and farsightedness of the algorithm. We demonstrate the effectiveness of our approach by running experiments on a small set of Atari games with state dependent safety labels. We present preliminary results that show our approximate shielding algorithm effectively reduces the rate of safety violations, and in some cases improves the speed of convergence and quality of the final agent.

Abstract: The terms 'semantics' and 'ontology' are increasingly appearing together with 'explanation', not only in the scientific literature, but also in organizational communication. However, all of these terms are also being significantly overloaded. In this paper, we discuss their strong relation under particular interpretations. Specifically, we discuss a notion of explanation termed ontological unpacking, which aims at explaining symbolic domain descriptions (conceptual models, knowledge graphs, logical specifications) by revealing their ontological commitment in terms of their assumed truthmakers, i.e., the entities in one's ontology that make the propositions in those descriptions true. To illustrate this idea, we employ an ontological theory of relations to explain (by revealing the hidden semantics of) a very simple symbolic model encoded in the standard modeling language UML. We also discuss the essential role played by ontology-driven conceptual models (resulting from this form of explanation processes) in properly supporting semantic interoperability tasks. Finally, we discuss the relation between ontological unpacking and other forms of explanation in philosophy and science, as well as in the area of Artificial Intelligence.