
## 8. Summary And Future Work

Agent tenology is growing rapidly in popularity  To handle real world domains and in teractions with people agents must be adaptable predictable rapidly responsive  An approach to resolving these potentially conflicting requirements is presented here  In sum mary  we have shown that certain machine learning operators are a priori  with no run time reverification safe to perform In other words when certain desirable properties hold prior to learning  they are guaranteed to hold post learning  The property classes considered here are Invariance and Response Learning operators delete spec delete vspec and delete - action were found to preserve properties in either of these classes  For lagent and lplan where there is a single  multi agent FSA plan   delete+gen   spec+gen and  stay were found to preserve Invariance properties  All of the a priori results are independent of the size of the FSA and are therefore applicable to any FSA that has been model checked originally We then discussed transformations of learning operators and their corresponding a priori results to a product plan   This addresses multplan's where multiple agents each have their own plan but the multiagent plan must be re formed and reverified to determine whether multiagent properties are preserved  It was discovered that only   delete delete Vspec spec and delete-action preserve their a priori results for this situation Finally  we presented novel incremental reverification algorithms for all cases in which the a priori results are negative It was shown in both theoretical and empirical comparisons that these algorithms can substantially improve the time complexity of reverification over total reverification from scratch Empirical results showed as much as a 4 billion fold speedup These are initial results  but continued research along these lines will likely be applicable to a wide range of important problems  including a variety of agent domains as well as more general software applications When learning is required we suggest that the a priori results should be consulted first If no positive results ie the learning operator is an SML exist then incremental reverification proceeds To test our overall framework  we have implemented the rovers example of this paper as co evolving agents assuming multpans  ie   multiple agents each with its own plan By using the a priori results and incremental algorithms  we achieved significant speedups We have also developed a more sophisticated application that uses reverification during evolution Two agents compete in a board game and one of the agents evolves its strategy to improve it The key lesson that has been learned from this implementation is that although the types of FSAs and learning operators are slightly different from those presented in this paper  and the property is quite different  it is a eck for a certain type of cyclic behavior on the board   initial experiences show that the methodology and basic results here could potentially be easily extended to a variety of multiagent applications Future work will focus primarily on extending the a priori results to other learning operators methods and property classes  developing other incremental reverification algo rithms  and exploring plan repair to recover from reverification failures  One way in which the a priori results might be extended is by discovering when learning operators will make a property true  even if it was not true before learning A question that was not addressed here is whether the incremental methods are useful if multiple machine learning operators are applied in batch  e g   as one might wish to do with