Dynamic Expansion of Behaviour Trees

Abstract

Artificial intelligence in games is typically used for creating player's opponents. Manual edition of intelligent behaviors for Non-Player Characters (NPCs) of games is a cumbersome task that needs experienced designers. Our research aims to assist designers in this task. Behaviours typically use recurring patterns, so that experience and reuse are crucial aspects for behavior design. The use of hierarchical state machines allows working on different abstraction levels, sharing transitions and reusing pieces from the more detailed levels. However, the static nature of the design process does not release the designer from the burden to completely specify each behaviour. Our approach applies Case-Based Reasoning (CBR) techniques to retrieve and reuse stored behaviors represented as hierarchical state machines (actually, behaviour trees). In this paper we focus on dynamic retrieval of behaviours taking into account the world state and the underlying goals to select the most appropriate state machine to guide the NPC behaviour. The global behaviour of the NPC is dynamically built in run time querying the CBR system. We exemplify our approach through a serious game, developed by our research group, with gameplay elements from First-Person Shooter (FPS) games.

A Novel Approach for Contextual Clustering and Retrieval of Behavior Trees to Enrich the Behavior of Social Intelligent Agents

“…None of the works in the literature review addressed the retrieval time of multibranched BTs based on certain criteria or similarity basis. However, working with BTs, in general, has posed some challenges, such as the exponential time when re-ordering BTs based on Q-values [20][21][22][23][24][25][26][27][28], high cost of 3.6 million seconds when developing AIbots in games using evolving behavior trees on sequential computers [29][30][31], and slow response during execution when building a case-based planner by learning and refining existing BTs [46][47][48][49]. Accordingly, the proposed technique in this paper can be seen as an advancement over existing works when it comes to time efficiency when working with multi-branched BTs.…”

“…CBR is an automated decision-making process where solutions to new problems are provided through experiences from problems that were previously encountered. Following this process, rules can be constructed that may solve future problems by retrieving and reusing stored behaviors [45][46][47]. One drawback of this method is the difficulty of refining existing strategies that have been previously learned from experience [45,46], but it has been shown that some additional adaptability can be provided using RL [48].…”

“…process, rules can be constructed that may solve future problems by retrieving and reusing stored behaviors [45][46][47]. One drawback of this method is the difficulty of refining existing strategies that have been previously learned from experience [45,46], but it has been shown that some additional adaptability can be provided using RL [48].…”