AlphaSIP (Spain)

In this paper we present a new search planning method for a coordinated swarm of UAVs based on the Theory of Search which provides a precise and robust probabilistic model for Search and Rescue (SAR) operations where lost victims need to be found as soon as possible. Using any “a priori” information about the victims' positions, a probability density function for each one is built and used to compute the optimal search effort allocation for the resources available. The priority assigned to each region of the area of interest is derived for such allocation using probability theory. This defines a set of priority sub-areas that span all the possible locations where a victim could be located. The optimal UAV distribution and order at which each sub-area is visited is computed using a Traveling Salesman Problem solver. The coverage paths within each sub-area are computed using an energy-aware path planner. We also address how to solve potential collisions with the terrain and/or other UAVs of the team. We have performed extensive simulations to validate our approach obtaining promising results in terms of probability of finding the victims and path feasibility.

The development of a sense of agency is indispensable for a cognitive entity (biological or artificial) to become a cognitive agent. In developmental psychology, researchers have taken inspiration from adult cognitive psychology and neuroscience literature and use the comparator model to assess the presence of a sense of agency in early infancy. Similarly, robotics researchers have taken components of the proposed mechanism in attempts to build a sense of agency into artificial systems. In this article, we identify an invalidating theoretical flaw in the reasoning underlying this conversion from adult studies to developmental science and cognitive systems research, rooted in an oversight in the conceptualization of the comparator model as currently used in experimental practice. In these experiments, the emphasis has been put solely on testing for a match between predicted and observed sensory consequences. We argue that the match by itself can exclusively generate a simple categorization or a representation of equality between predicted and observed sensory consequences, both of which are insufficient to generate the causal representations required for a sense of agency. Consequently, the comparator model, as it has been described in the context of the sense of agency and as it is commonly used in experimental designs, is insufficient to generate the sense of agency: infants and robots require more than developing the ability to match predicted and observed sensory consequences for a sense of agency. We conclude with outlining possible solutions and future directions for researchers in developmental science and artificial intelligence.