Davide Aversa

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In many navigational domains the traversability of cells is conditioned on the path taken. This is often the case in videogames, in which a character may need to acquire a certain object (i.e., a key or a flying suit) to be able to traverse specific locations (e.g., doors or high walls). In order for non-player characters to handle such scenarios we present(More)
In this paper we propose an architecture for specifying the interaction of non-player characters (NPCs) in the game-world in a way that abstracts common tasks in four main conceptual components, namely perception, deliberation, control, action. We argue that this architecture, inspired by AI research on autonomous agents and robots, can offer a number of(More)
We devise a succinct knowledge representation framework based on a nondeterministic variant of a well-known Artificial Intelligence formalism, called PDDL, for representing dynamic domains in Planning. We represent the environment and the agents' (high-level) behavior as distinct PDDL action domains and exploit service composition techniques, to compose(More)
Inventory-Aware Pathfinding is concerned with finding paths while taking into account that picking up items, e.g., keys, allow the character to unlock blocked pathways, e.g., locked doors. In this work we present a pruning method and a preprocessing method that can improve significantly the scalability of such approaches. We apply our methods to the recent(More)
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