Learning of Behavior Trees for Autonomous Agents

Learning of Behavior Trees for Autonomous Agents

Definition of an accurate system model for Automated Planner (AP) is often impractical, especially for real-world problems. Conversely, off-the-shelf planners fail to scale up and are domain dependent. These drawbacks are inherited from conventional transition systems such as Finite State Machines (FSMs) that describes the action-plan execution generated by the AP. On the other hand, Behavior T...

How Human Inspired Learning Enhances the Behavior of Autonomous Agents

An autonomous agent must deal with unforeseen situations that can’t be preprogrammed. Therefore, the agent has to make its own experiences, solutions and valuations to situations, actions and objects to be able to enhance previous actions and avoid repeating wrong actions. On the basis of the existing cognitive architecture Simulation of Mental Apparatus and Applications (SiMA) at the Institute...

Autonomous Behavior of Computational Agents

In this paper we present an architecture for decision making of software agents that allows the agent to behave autonomously. Our target area is computational agents — encapsulating various neural networks, genetic algorithms, and similar methods — that are expected to solve problems of different nature within an environment of a hybrid computational multi-agent system. The architecture is base...

Simulated Autonomous Agents Utilizing Instincts and Behavior Learning:

Adaptive Behavior in Autonomous Agents

This paper gives an overview of the bottom-up approach to artificial intelligence (AI), commonly referred to as behavior-oriented AI. The behavior-oriented approach, with its focus on the interaction between autonomous agents and their environments, is introduced by contrasting it with the traditional approach of knowledge-based AI. Different notions of autonomy are discussed, and key problems ...