Creative behavior is one of the most fascinating areas in intelligence. The development of specific styles is the most characteristic feature of creative behavior. All important creators, such as Picasso and Beethoven, have their own distinctive styles that even non-professional art lovers can easily recognize. Hence, in the present work, attempting to achieve cantus firmus composition and style development as well as inspired by the behavior of natural ants and the mechanism of ant colony optimization (ACO), this paper firstly proposes a meta-framework, called ants on multiple graphs (AntsOMG), mainly for roughly modeling creation activities and then presents an implementation derived from AntsOMG for composing cantus firmi, one of the essential genres in music. Although the mechanism in ACO is adopted for simulating ant behavior, AntsOMG is not designed as an optimization framework. Implementations can be built upon AntsOMG in order to automate creation behavior and realize autonomous development on different subjects in various disciplines. In particular, an implementation for composing cantus firmi is shown in this paper as a demonstration. Ants walk on multiple graphs to form certain trails that are composed of the interaction among the graph topology, the cost on edges, and the concentration of pheromone. The resultant graphs with the distribution of pheromone can be interpreted as a representation of cantus firmus style developed autonomously. Our obtained results indicate that the proposal has an intriguing effect, because significantly different styles may be autonomously developed from an identical initial configuration in separate runs, and cantus firmi of a certain style can be created in batch simply by using the corresponding outcome. The contribution of this paper is twofold. First, the presented implementation is immediately applicable to the creation of cantus firmi and possibly other music genres with slight modifications. Second, AntsOMG, as a meta-framework, may be employed for other kinds of autonomous development with appropriate implementations.

This paper presents a study on fusing creative operations into evolutionary computation for music composition, as an attempt to provide a collaborative creation environment between human and machine and to investigate into the mind process during music creation. A framework based on the paradigm of evolutionary computation is proposed to incorporate key mechanisms observed in the creation process and implemented to work on evolving abstract musical ideas and thoughts. The developed software framework is released as open source. The collaborative creation process, involving the framework, the composer, and the performer, is presented and discussed, and the results of this study also include a composed music work for unaccompanied cello, played by a renowned cellist.

This position paper contends that as artificial intelligence (AI) and robotics advance at a rapid pace—transforming industries, healthcare, transportation, and more—the development of structured knowledge and formal ontologies struggles to keep up. This imbalance poses substantial risks, as increasingly powerful and autonomous systems can make decisions that are both opaque and difficult to verify. While AI and robotics research benefit from significant funding and widespread attention, ontology and structured knowledge efforts often remain under-resourced, under-integrated, and often sidelined—leaving a growing semantic gap in system design and governance. To address this gap, we argue that formal ontology research must be reinvigorated to keep pace with the accelerating demands of AI and robotics—not merely as a support function but as a core contributor to trustworthy and beneficial AI/robotics system design. This requires renewed investment in the academic foundations of ontology engineering, reintegration of semantic modeling into AI/robotics development workflows, and the lowering of practical barriers that have hindered broader adoption. Specifically, we advocate for ontology-augmented verification methods that incorporate semantic constraints into behavioral validation; for the institutionalization of semantic auditing practices that allow ontologies to serve as transparent, inspectable system referents; and for the adoption of FAIR publishing standards that treat ontologies as first-class research outputs. These directions, we argue, are essential to bridge the gap between AI/robotics and ontology research and to ensure that autonomous AI/robotics systems remain not only performant but also transparent, accountable, and ultimately beneficial to humanity.