Andrea Rafanelli

Humans quickly learn new concepts from a small number of examples. Replicating this capacity with Artificial Intelligence (AI) systems has proven to be challenging. When it comes to learning subjective tasks-where there is an evident scarcity of data-this capacity needs to be recreated. In this work, we propose an intuitive human-agent teaching architecture in which the human can teach an agent how to perform a task by providing demonstrations, i.e., examples. To have an intuitive interaction, we argue that the agent should be able to learn incrementally from a few single examples. To allow for this, our objective is to broaden the agent's task understanding using domain knowledge. Then, using a learning method to enable the agent to learn efficiently from a limited number of examples. Finally, to optimize how human can select the most representative and less redundant examples to provide the agent with. We apply our proposed method to the subjective task of ingredient substitution, where the agent needs to learn how to substitute ingredients in recipes based on human examples. We replicate human input using the Recipe1MSubs dataset. In our experiments, the agent achieves half its task performance after only 100 examples are provided, compared to the complete training set of 50k examples. We show that by providing examples in strategic order along with a learning method that leverages external symbolic knowledge, the agent can generalize more efficiently.

Ischaemic heart diseases (IHDs) encompass a range of conditions, among which ST-segment elevation myocardial infarction (STEMI) represents a particularly severe form. STEMI is characterised by a complete occlusion of a coronary artery, and it benefits from rapid revascularisation through primary percutaneous coronary intervention (PCI) within 120 min from ECG-based diagnosis.1 Meeting these time requirements not only necessitates a rapid diagnosis but also quick identification of the culprit vessel so that appropriate treatment can be delivered promptly.

Bridging intelligent symbolic agents and sub-symbolic predictors is a long-standing research goal in AI. Among the recent integration efforts, symbolic knowledge injection (SKI) proposes algorithms aimed at steering sub-symbolic predictors’ learning towards compliance w.r.t. pre-existing symbolic knowledge bases. However, state-of-the-art contributions about SKI mostly tackle injection from a foundational perspective, often focussing solely on improving the predictive performance of the sub-symbolic predictors undergoing injection. Technical contributions, in turn, are tailored on individual methods/experiments and therefore poorly interoperable with agent technologies as well as among each others. Intelligent agents may exploit SKI to serve many purposes other than predictive performance alone—provided that, of course, adequate technological support exists: for instance, SKI may allow agents to tune computational, energetic, or data requirements of sub-symbolic predictors. Given that different algorithms may exist to serve all those many purposes, some criteria for algorithm selection as well as a suitable technology should be available to let agents dynamically select and exploit the most suitable algorithm for the problem at hand. Along this line, in this work we design a set of quality-of-service (QoS) metrics for SKI, and a general-purpose software API to enable their application to various SKI algorithms—namely, platform for symbolic knowledge injection (PSyKI). We provide an abstract formulation of four QoS metrics for SKI, and describe the design of PSyKI according to a software engineering perspective. Then we discuss how our QoS metrics are supported by PSyKI. Finally, we demonstrate the effectiveness of both our QoS metrics and PSyKI via a number of experiments, where SKI is both applied and assessed via our proposed API. Our empirical analysis demonstrates both the soundness of our proposed metrics and the versatility of PSyKI as the first software tool supporting the application, interchange, and numerical assessment of SKI techniques. To the best of our knowledge, our proposals represent the first attempt to introduce QoS metrics for SKI, and the software tools enabling their practical exploitation for both human and computational agents. In particular, our contributions could be exploited to automate and/or compare the manifold SKI algorithms from the state of the art. Hence moving a concrete step forward the engineering of efficient, robust, and trustworthy software applications that integrate symbolic agents and sub-symbolic predictors.