A simheuristic approach for evolving agent behaviour in the exploration for novel combat tactics

The automatic generation of behavioural models for intelligent agents in military simulation and experimentation remains a challenge. Genetic Algorithms are a global optimization approach which is suitable for addressing complex problems where locating the global optimum is a difficult task. Unlike traditional optimisation techniques such as hill-climbing or derivatives-based methods, Genetic Algorithms are robust for addressing highly multi-modal and discontinuous search landscapes. In this paper, we outline a simheuristic GA-based approach for automatic generation of finite state machine based behavioural models of intelligent agents, where the aim is the identification of novel combat tactics. Rather than evolving states, the proposed approach evolves a sequence of transitions. We also discuss workable starting points for the use of Genetic Algorithms for such scenarios, shedding some light on the associated design and implementation difficulties

Specifying Requirements in a Multi-Agent System with Use Cases

The software engineering of multi-agent systems demands specification of the required agent behaviours to provide documented requirements for the design and implementation phases. A methodology for the analysis and specification of agent behaviours is proposed, which arises from a lengthy experience in the construction of multi-agent simulations for military operations research. The methodology builds upon the existing use case modelling techniques provided by the Unified Modeling Language (UML) and is in keeping with the agent extensions to the UML proposed elsewhere. A case-study from a specific multi-agent air combat simulation accompanies the elaboration of the methodology

Discovering emergent agent behaviour with evolutionary finite state machines

In this paper we introduce a novel approach to discovering emergent behaviour in multiagent simulations, using evolutionary finite state machines to model intelligent agents in an adversarial two-player game. Agent behaviour is modelled as a finite set of predetermined states. The logic that leads to transitions between states is evolved to maximise fitness, which is determined through execution in a constructive simulation environment. The resultant evolved finite state machine (E-FSM) is evaluated for two finite state machine implementations, one with states specifically designed to perform a known behaviour and the other with states consisting of generic actions. Our experiments demonstrate that this approach can discover complex emergent behaviours from simple, generic actions, and use these behaviours to achieve a position of tactical superiority in the domain of air combat simulation

Towards reuse in agent oriented information systems: the importance of being purposive

The emergence of large information systems has pushed software specification into the area of business modelling to adequately capture and consider business requirements. At the same time, there has been a move toward techniques for specifying the behaviours of and the knowledge associated with intelligent agents as these are increasingly found as important components of those information systems. This paper presents four software models useful for specifying the requirements of an agent oriented information system. Adopting a similar notation for each model smooths the transition between models. It will be shown that it is in the relationships between these models there is scope for capturing purposive descriptions that facilitate reuse at various levels. A commentary on the importance of an explicit representation of the purpose for which a software component is intended is provided followed by an example, from the domain of military simulation, that illustrates the model and its application. The aim of this paper is to present a, modelling approach that unifies business models, use case models, agent behavioural models and domain models, for the purpose of specifying an agent oriented information system

Behaviour recognition with kinodynamic planning over continuous domains

We investigate the application of state-of-the-art goal recognition techniques for behaviour recognition over complex continuous domains using model predictive control (MPC) for trajectory generation. We formally define the problem of kinodynamic behaviour recognition and establish a set of baseline behaviours and performance measures in the complex domain of unmanned aerial maneuvers. We evaluate how well our approach performs over a range of standard aerial maneuvers and representative initial configurations of varying complexity. The work also highlights future research directions in compound model-based behaviour recognition and team behaviour recognition where multiple agents may be acting simultaneously

Towards Reuse in Agent Oriented Information Systems:

The emergence of large information systems has pushed software specification into the area of business modelling to adequately capture and consider business requirements. At the same time, there has been a move toward techniques for specifying the behaviours of and the knowledge associated with intelligent agents as these are increasingly found as important components of those information systems. This paper presents four software models useful for specifying the requirements of an agent oriented information system. Adopting a similar notation for each model smooths the transition between models. It will be shown that it is in the relationships between these models there is scope for capturing purposive descriptions that facilitate reuse at various levels. A commentary on the importance of an explicit representation of the purpose for which a software component is intended is provided followed by an example, from the domain of military simulation, that illustrates the model and its application. The aim of this paper is to present a modelling approach that unifies business models, use case # Simon Goss is a research fellow at The Department of Computer Science and Software Engineering, at the University of Melbourne + Clint Heinze is a post-graduate student in the Intelligent Agent Lab, Department of Computer Science and Software Engineering, The University of Melbourne ([email protected]) Michael Papasimeon is a post-graduate student in the Intelligent Agent Lab, Department of Computer Science and Software Engineering, at The University of Melbourne ([email protected]) models, agent behavioural models and domain models, for the purpose of specifying an agent oriented information system. 1