Group Coordination in Multi-Agent Systems: Is it a necessity or an overhead?

Traditionally, defence analysts adopted what are known as Lanchester Equations to model and theorise about combat attrition, such as ELAN, JANUS, CASTFORME, ModSAF and OneSAF. With the recognition of the limitations of Lanchester Equations, the idea that combat can be modelled as a complex adaptive system has widely been accepted recently. This has led to the emergence of a number of multi-agent combat systems, or agent-based distillation systems (ABDs), such as ISAAC (Ilachinski 2000), EINSTein (Ilachinski 1999; Ilachinski 2004), MANA (Lauren and Stephen 2002), BactoWars (White 2004) and CROCADILE (Barlow and Easton 2002). These ABDs have facilitated the analysis and understanding of combat. However, these ABDs adopted the reactive agent architecture, where agents only react to their environment; (i.e. there is no coordination mechanism among the agents). The lack of planning and coordination limited the capacity of these ABDs to study group behaviours in combat. The concept of fitness landscape was first introduced by Wright (1932) in biology to represent adaptive evolution as the population navigates on a mountainous surface where the height of a point specifies how well the corresponding organism is adapted to its environment. The structure and property of the fitness landscape play a major role in determining the success of the search method and the degree of problem difficulty (Horn and Goldberg 1999; Kallel et al. 2001; Teo and Abbass 2003; Vassilev and Miller 2000; Stadler 2002). In this paper we analyse the fitness landscapes generated by ABD with coordination mechanism and compare those with those from ABD without coordination mechanism to identify if group coordination is a necessity in agentbased combat modelling. We adopt version II of WISDOM (the Warfare Intelligent System for Dynamic Optimisation of Missions) (Yang et al. 2005; Yang et al. 2006; Yang et al. 2006b) as a simulation platform. It is built on a novel multi-agent architecture, called NCMAA (Network Centric Multi-Agent Architecture) (Yang et al. 2005) which allows the analyst to easily model group coordination in multi-agent system. Three scenarios: Goal Oriented (GOL), Aggressive (AGG) and Defensive (DEF) are created as in (Yang et al. 2006a). The configurations of the environment, initial position and the number of agents are the same as in (Yang et al. 2006a). Each scenario involves repeating the simulation 100 repeats, each for 500 time steps. The same objective function and fitness function are adopted as in (Yang et al. 2006a). And ten different random walks are taken, each of length 10,000 solutions using two fitness functions (average and normalized). Each stochastic neighbourhood in the search space was obtained by adding a random number drawn from a Gaussian distribution with zero mean and 0.1 standard deviation to each variable in the genotype. If the value of any personality is out of the range [−1, 1], the value is truncated. The generated fitness landscape associated with each scenario is then analysed by using information analysis approach proposed by Vassilev, Fogarty, and Miller (2000) to characterise the search space. The results are compared with those from WISDOM-I which does not have a coordination mechanism (Yang, Abbass, and Sarker 2006a). The difference conforms that modelling group coordination is crucial in ABDs. Although the findings are based on an analysis in the defence domain, it can also be extended and applied into other domains. For example, in water resource management, if each end user is modelled as an agent, coordination among agents in the same state, same city or same suburb should be modelled and interactions between states, cities and suburbs can then be studied.