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In both applied ontology and engineering, functionality is a well-researched topic, since it is through teleological causal reasoning that domain experts build mental models of engineering systems, giving birth to functions. These mental models are important throughout the whole lifecycle of any product, being used from the design phase up to diagnosis activities. Though a vast amount of work to model functions has already been carried out, the literature has not settled on a shared and well-defined approach due to the variety of concepts involved and the modeling tasks that functional descriptions should satisfy. The work in this paper posits the basis and makes some crucial steps towards a rich ontological description of functions and related concepts, such as behaviour, capability, and capacity. A conceptual analysis of such notions is carried out using the top-level ontology DOLCE as a framework, and the ensuing logical theory is formally described in first-order logic and OWL, showing how ontological concepts can model major aspects of engineering products in applications. In particular, it is shown how functions can be distinguished from the implementation methods to realize them, how one can differentiate between capabilities and capacities of a product, and how these are related to engineering functions.

When you meet a delivery robot in a narrow street it stops to let you pass. It was built to give you precedence. What happens if you run into a robot that was not trained by or for humans? The existence in our environment of robots which do not abide by human behavioral rules and social systems might sound odd, but is a case we may encounter in the future. In this paper, self-taught robots are artificial embodied agents that, thanks for instance to AI learning techniques, manage to survive in the environment without embracing behavioral or judgment rules given and used by humans. The paper argues that our ontological systems are not suitable to understand and cope with artificial agents. The arguments are speculative rather than empirical, and the goal is to drive attention to new ontological challenges.

We aim to show that far-related primates like humans and the capuchin monkeys show interesting correspondences in terms of artifact characterization and categorization. We investigate this issue by using a philosophically-inspired definition of physical artifact which, developed for human artifacts, turns out to be applicable for cross-species comparison. In this approach an artifact is created when an entity is intentionally selected and some capacities attributed to it (often characterizing a purpose). Behavioral studies suggest that this notion of artifact is not specific to the human kind. On the basis of the results of a series of field observations and experiments on wild capuchin monkeys that routinely use stone hammers and anvils, we show that the notions of intentional selection and attributed capacity appear to be at play in capuchins as well. The study also suggests that functional criteria and contextualization play a fundamental role in terms of artifact recognition and categorization in nonhuman primates.

We introduce quantificational modal operators as dynamic modalities with (extensions of) Henkin quantifiers as indices. The adoption of matrices of indices (with action identifiers, variables and/or quantified variables as entries) gives an expressive formalism which is here motivated with examples from the area of multi-agent systems. We study the formal properties of the resulting logic which, formally speaking, does not satisfy the normality condition. However, the logic admits a semantics in terms of (an extension of) Kripke structures. As a consequence, standard techniques for normal modal logic become available. We apply these to prove completeness and decidability, and to extend some standard frame results to this logic.