In this paper, two behavior control architectures for autonomous agents in the form of cross-platform C++ frameworks are presented, the State Controller Library and the Behavior Control Framework. While the former is state-based and generalizes the notion of states and finite state machines to allow for multi-action planning, the latter is behavior-based and exploits a hierarchical structure and the concept of inhibitions to allow for dynamic transitioning. The two frameworks have completely independent implementations, but can be used effectively in tandem to solve behavior control problems on all levels of granularity. Both frameworks have been used to control the NimbRo-OP, a humanoid soccer robot developed by team NimbRo of the University of Bonn.
Maes developed the Agent Network Architecture (ANA) @cite_13 , @cite_21 , also a behavior-based artificial intelligence approach. In this architecture, the agent consists of a distributed and decentralized collection of primitive behaviors, referred to as . The competence modules are divided into groups of incompatible behaviors, and interact based on a network of predecessor, successor and conflictor links in such a way that the various modules are activated and inhibited dynamically when appropriate. Each competence module only implements a single basic primitive behavior however, so the activation network would grow quite large for real-size problems and become slow and overwhelmed by details @cite_13 . Also, it is a difficult task to tune all of the network parameters and activation functions so that the appropriate module(s) for a given situation in general tend to be activated.
@cite_21 @cite_13
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This paper discusses the problem of action selection for an autonomous agent. We argue that the so-called ''situated agents'', which have been built in response to the limitations observed with classical planners, do not provide a satisfactory solution to this problem because of their lack of goals and run-time arbitration. We present a novel action selection theory which allows arbitration among goals and actions while producing fast and robust activity in a tight interaction loop with the environment. The theory models action slection as an emergent property of an activation inhibition dynamics among the actions the agent can select and between the actions and the environment. A handful of global parameters make it possible to smoothly mediate between several action selection criteria. For example, one can balance goal-orientedness against situation-orientedness, bias towards ongoing plans (inertia) against adaptivity, thoughtfulness against speed, and adjust sensitivity to goal conflicts. The goal of my work is to develop and implement an architecture for an autonomous agent, which I refer to as "ANA". An ANA agent consists of a distributed set of "competence modules". Competence modules are linked in a network. A spreading activation process operates on the network to decide what the "relevance" or relative strength of a competence module is in the current context. This process implements a competition among modules for activation energy. The higher the activation energy level of a module, the more likely it is that this module determines what the autonomous agent does or communicates to believe. Learning is a central, completely integrated feature of the architecture. The competence module network is continuously being developed and changed on the basis of experience: links are added and deleted depending on real world observations and new "macro modules" are created whenever a goal is achieved. This paper presents an overview of the architecture. It describes the functionalities that have been implemented, the results that have been obtained with robotic and simulated ANA agents, and finally it discusses (current) limitations and future work.