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Time and space are fundamental to human language and embodied cognition. In our early work we investigated how Lingodroids, robots with the ability to build their own maps, could evolve their own geopersonal spatial language. In subsequent studies we extended the framework developed for learning spatial concepts and words to learning temporal intervals. This paper considers a new aspect of time, the naming of concepts like morning, afternoon, dawn, and dusk, which are events that are part of day-night cycles, but are not defined by specific time points on a clock. Grounding of such terms refers to events and features of the diurnal cycle, such as light levels. We studied event-based time in which robots experienced day-night cycles that varied with the seasons throughout a year. Then we used meet-at tasks to demonstrate that the words learned were grounded, where the times to meet were morning and afternoon, rather than specific clock times. The studies show how words and concepts for a novel aspect of cyclic time can be grounded through experience with events rather than by times as measured by clocks or calendars. [ABSTRACT FROM PUBLISHER]

An understanding of time and temporal concepts is critical for interacting with the world and with other agents in the world. What does a robot need to know to refer to the temporal aspects of events—could a robot gain a grounded understanding of “a long journey,” or “soon?” Cognitive maps constructed by individual agents from their own journey experiences have been used for grounding spatial concepts in robot languages. In this paper, we test whether a similar methodology can be applied to learning temporal concepts and an associated lexicon to answer the question “how long” did it take to complete a journey. Using evolutionary language games for specific and generic journeys, successful communication was established for concepts based on representations of time, distance, and amount of change. The studies demonstrate that a lexicon for journey duration can be grounded using a variety of concepts. Spatial and temporal terms are not identical, but the studies show that both can be learned using similar language evolution methods, and that time, distance, and change can serve as proxies for each other under noisy conditions. Effective concepts and names for duration provide a first step towards a grounded lexicon for temporal interval logic. [ABSTRACT FROM PUBLISHER]