is visited infinitely often ie  it is not in a cycle  
then and therefore and verification succeeds  A relatively efficient i e algorithm for AT verification from the literature is presented in Section

## 3.4  Machine Learning To Adapt Plans

Given plan and property model checking determines whether Next we consider the case of learning  which is a change to     This subsection addresses the issue of how a learning operator can affect a plan to generate a new plan We begin by presenting a taxonomy of FSA learning operators  It is likely that any learning method for complete deterministic FSAs will be composed of one or more of these operators Nothing about our approach requires evolutionary learning per se  however to make the discussion concrete  this is the form of learning that is assumed here   In the context of evolutionary algorithms the FSA learning operators are perturbations such as mutations  applied to the FSAs Procedure EA
t = 0; /* initial generation */
initialize_population(t);
eva luate_fitness( t );
until termination-criterion do t = t + 1; /* next generation */ select_parents( t );
perturb( t );
evaluate_fitness(t);
enduntil end procedure Figure The outline of an evolutionary algorithm We assume that learning occurs in two phases  the offline and online phases  see Fig During the offline phase  each agent starts with a randomly initialized population of ure candidate FSA plans This population is evolved using the evolutionary algorithm outlined The main loop of this algorithm consists of selecting parent plans from the in Figure population  applying perturbation operators to the parents to produce offspring  evaluat ing the fitness of the offspring  and then returning the offspring to the population if they are sufficiently fit  After this evolution  verification and repair are done to these initially gnnrated plans At the start of the online phase  each agent selects one   best"   according to its   fitness function"  plan from its population for execution  The agents are then fielded and plan execution is interleaved with learning adaptation reverification and plan repair as needed The purpose of learning during the online phase is to fine tune the plan and adapt it to keep pace with a gradually shifting environment since normally real world environments are not static The evolutionary algorithm of Figure is also used during this phase but the assumption is a population size of one and incremental learning ie one learning operator