Agent Theories, Architectures, and Languages: A Survey <s> Planning agents <s> We describe a new problem solver called STRIPS that attempts to find a sequence of operators in a space of world models to transform a given initial world model into a model in which a given goal formula can be proven to be true. STRIPS represents a world model as an arbi trary collection of first-order predicate calculus formulas and is designed to work with models consisting of large numbers of formulas. It employs a resolution theorem prover to answer (juestions of particular models and uses means-ends analysis to guide it to the desired goal-satisfying model. <s> BIB001 </s> Agent Theories, Architectures, and Languages: A Survey <s> Planning agents <s> Abstract A problem domain can be represented as a hierarchy of abstraction spaces in which successively finer levels of detail are introduced. The problem solver ABSTRIPS, a modification of STRIPS, can define an abstraction space hierarchy from the STRIPS representation of a problem domain, and it can utilize the hierarchy in solving problems. Examples of the system's performance are presented that demonstrate the significant increases in problem-solving power that this approach provides. Then some further implications of the hierarchical planning approach are explored. <s> BIB002 </s> Agent Theories, Architectures, and Languages: A Survey <s> Planning agents <s> IPEM, for Integrated Planning, Execution and Monitoring, provides a simple, clear and well defined framework to integrate these processes. Representation integration is achieved by naturally incorporating execution and monitoring information into [Chapman, 1987] tweak's partial plan representation. Control integration is obtained by using a production system architecture where IF-THEN rules, referred to as flaws and fixes, specify partial plan transformations. Conflict resolution is done using a scheduler that embodies the current problem solving strategy. ::: ::: Since execution and plan elaboration operations have been designed to be independently applicable, and execution of an action is a scheduling decision like any other, the framework effectively supports interleaving of planning and execution (IPE). This renders a local ability to replan after both unexpected events and execution failure. ::: ::: The framework has served as the basis for an implemented hierarchical, nonlinear planning and execution system that has been tested on numerous examples, on various domains, and has shown to be reliable and robust. <s> BIB003 </s> Agent Theories, Architectures, and Languages: A Survey <s> Planning agents <s> Describing the artificial intelligence planning program, AUTODRIVE, this work integrates classical and reactive approaches to planning and decision making and shows how to use plan recognition when dealing with a situation which contains a large degree of uncertainty. New concepts are introduced regarding the way we think of plan elaboration, and a working computer program which shows how artificial planning can operate in practice, is described. <s> BIB004
Since the early 1970s, the AI planning community has been closely concerned with the design of artificial agents; in fact, it seems reasonable to claim that most innovations in agent design have come from this community. Planning is essentially automatic programming: the design of a detailed course of action which, when executed, will result in the achievement of some desired goal. Within the symbolic AI community, it has long been assumed that some form of AI planning system will be a central component of any artificial agent. Perhaps the best know early planning system was STRIPS BIB001 . This system takes a symbolic descripion of both the world and a desired goal state, and a set of action descriptions, which characterise the pre-and post-conditions associated with various actions. It then attempts to find a sequence of actions that will achieve the goal, by using a simple means-ends analysis, which essentially involves matching the post-conditions of actions against the desired goal. The STRIPS planning algorithm was very simple, and proved to be ineffective on problems of even moderate complexity. Much effort was subsequently devoted to developing more effective automatic planning techniques. Two major innovations were hierarchical and non-linear planning BIB002  . However, in the mid 1980s, Chapman established some theoretical results which indicate that even such refined techniques will ultimately turn out to be unusable in any time-constrained system  . These results have had a profound influence on subsequent AI planning research; perhaps more than any other results, they have caused some researchers to question the whole symbolic AI paradigm, and have thus led to the work on alternative approaches that we discuss in section 3.2. In spite of these difficulties, various attempts have been made to construct agents whose primary component is a planner. For example: the Integrated Planning, Execution and Monitoring (IPEM) system is based on a sophisticated non-linear planner BIB003 ; Wood's AUTODRIVE system has planning agents operating in a highly dynamic environment (a traffic simulation) BIB004 .