this paper, I will argue that, contrary to these challenges, planning deserves its central place on the AI map. I will claim that intelligent agents are planning agents, and that philosophical and commonsense psychological theorizing about the process of planning can provide useful insights into the question of agent design. The theories I have in mind are not restricted to The Uses of Plans 3 how agents can form plans. Much of my research has concerned the ways in which intelligent agents use their plans. I will describe some of that research, and will argue that plans are used not only to guide action, but also to control reasoning and to enable inter-agent coordination. These uses of plans make possible intelligent behavior in complex, dynamic, multiagent environments. 2 Planning We can begin by asking what exactly we mean by "planning". For many years, planning had a quite specific meaning in AI: it was the process of formulating a program of action to achieve some specified goal. You gave a planning system a description of initial conditions and a goal, and it produced a plan of action whose execution in a state satisfying the initial conditions was guaranteed to result in a state satisfying the goal. These plans were akin to recipes for achieving the goal. Your goal might be to have a chocolate cake. In the initial state, you might have eggs, milk, and chocolate, a pan and a working oven. In these conditions, a valid plan might be to go the store to buy some flour, return home, preheat the oven, mix the ingredients, pour the mixture into the pan, and put it in the oven for 45 minutes. Traditional AI planning systems like STRIPS [22], NOAH [63], and SIPE [71], were designed to construct just this kind of plan---except usually the goal was something like a tower o...

The paradigms of transformational planning, case-based planning, and plan debugging all involve a process known as plan adaptation --- modifying or repairing an old plan so it solves a new problem. In this paper we provide a domain-independent algorithm for plan adaptation, demonstrate that it is sound, complete, and systematic, and compare it to other adaptation algorithms in the literature. Our approach is based on a view of planning as searching a graph of partial plans. Generative planning starts at the graph's root and moves from node to node using planrefinement operators. In planning by adaptation, a library plan---an arbitrary node in the plan graph---is the starting point for the search, and the plan-adaptation algorithm can apply both the same refinement operators available to a generative planner and can also retract constraints and steps from the plan. Our algorithm's completeness ensures that the adaptation algorithm will eventually search the entire graph and its systemat...

Heuristic search methods promise to find shortest paths for path-planning problems faster than uninformed search methods. Incremental search methods, on the other hand, promise to find shortest paths for series of similar path-planning problems faster than is possible by solving each path-planning problem from scratch. In this article, we develop Lifelong Planning A * (LPA*), an incremental version of A * that combines ideas from the artificial intelligence and the algorithms literature. It repeatedly finds shortest paths from a given start vertex to a given goal vertex while the edge costs of a graph change or vertices are added or deleted. Its first search is the same as that of a version of A * that breaks ties in favor of vertices with smaller g-values but many of the subsequent searches are potentially faster because it reuses those parts of the previous search tree that are identical to the new one. We present analytical results that demonstrate its similarity to A * and experimental results that demonstrate its potential advantage in two different domains if the path-planning problems change only slightly and the changes are close to the goal.

Case-based planning is the reuse of past successful plans in order to solve new planning problems. This paper presents a survey of case-based planning, in terms of its historical roots, underlying foundations, methods and techniques currently used, limitations, and future trends. Several authors have given overviews on case-based reasoning and specific topics such as case retrieval, case adaptation, and learning. This overview differs in focus. Its aim is to emphasize the case-based approach to planning, its methodological issues, and its relation to classical planning and the other kinds of case-based reasoning. It also provides some reference models. Keywords: Case-based planning, Case-based reasoning, Planning, Plan Retention, Plan Retrieval, Plan Reuse, Plan Revision. Statement of Exclusive Submission: This paper has not been submitted elsewhere in identical or similar form, nor will it be during the first three months after its submission to Artificial Intelligence Review. 1 Contents 1

for Correspondence An agent should adopt different control modes in different situations. Depending on the predictability of its environment and the constraint imposed by its goals, the agent should modulate its sensitivity to run-time events and its commitment to specific actions. We propose an opportunistic control model that supports this flexibility. 3 Abstract Given its multiple goals, limited resources, and dynamic environment, an intelligent agent must decide which of many possible actions to execute at each point in time. Planning and reactive models embody two different modes of control. By contrast, we characterize a two-dimensional space of control modes, each of which maximizes the quality of run-time behavior in the corresponding region of a two-dimensional space of control situations. The situation space is defined by dimensions representing the predictability of the agent's task environment and the constraint imposed by its goals. The space of control modes is defined ...

In experience-based (or case-based) reasoning, new problems are solved by retrieving and adapting the solutions to similar problems encountered in the past. An important issue in experience-based reasoning is to identify different types of knowledge and reasoning useful for different classes of case-adaptation tasks. In this paper, we examine a class of non-routine caseadaptation tasks that involve patterned insertions of new elements in old solutions. We describe a model-based method for solving this task in the context of the design of physical devices. The method uses knowledge of generic teleological mechanisms (GTMs) such as cascading. Old designs are adapted to meet new functional specifications by accessing and instantiating the appropriate GTM. The Kritik2 system evaluates the computational feasibility and sufficiency of this method for design adaptation. In the Proceedings of the Fourteenth Annual Conference of the Cognitive Science Society. Bloomington, Indiana, July 29 - Aug...

Conventional behaviors develop from practice for regularly occurring problems of coordination within a community of actors. Re-using and extending conventional methods for coordinating behavior is the task of everyday reasoning. The

It is often possible to envision the ways in which knowledge will be reused. By preparing the knowledge appropriately at storage time, we can simplify the later task of adapting the stored knowledge. Preparation can simplify, remove inefficiencies, and segment the trace data into useful ideas. In this paper, we apply this principle to a computational model that deals with the problem of a distributed collective memory for multi-agent systems and we provide technical detail about how the experience of the agents is prepared before storage. 1 Introduction We are interested in technical issues in building multiagent systems that use a distributed collective memory of previous problem-solving episodes. The basic idea is that a group of heterogeneous agents solve some collective problem. The resulting episode traces are divided up and stored in the collective memory of the problemsolving agents. In future episodes, these traces can be used, and adapted, so as to serve as a basis for improv...

A fundamental difficulty faced by groups of agents that work together is how to efficiently coordinate their efforts. This coordination problem is both ubiquitous and challenging, especially in environments where autonomous agents are motivated by personal goals. Previous AI

One method for making analogies is to access and instantiate abstract domain principles, and one method for acquiring knowledge of abstract principles is to discover them from experience. We view generalization over experiences in the absence of any prior knowledge of the target principle as the task of hypothesis formation, a subtask of discovery. Also, we view the use of the hypothesized principles for analogical design as the task of hypothesis testing, another subtask of discovery. In this paper, we focus on discovery of physical principles by generalization over design experiences in the domain of physical devices. Some important issues in generalization from experiences are what to generalize from an experience, how far to generalize, and what methods to use. We represent a reasoner's comprehension of specific designs in the form of structure-behavior-function (SBF) models. An SBF model provides a functional and causal explanation of the working of a device. We represe...