The key to utilizing the potential of multirobot systems is cooperation. How can we achieve cooperation in systems composed of failure-prone autonomous robots operating in noisy, dynamic environments? In this paper, we present a novel method of dynamic task allocation for groups of such robots. We implemented and tested an auction-based task allocation system which we call MURDOCH, built upon a principled, resource centric, publish /subscribe communication model. A variant of the Contract Net Protocol, MURDOCH produces a distributed approximation to a global optimum of resource usage. We validated MURDOCH in two very different domains: a tightly coupled multirobot physical manipulation task and a loosely coupled multirobot experiment in long-term autonomy. The primary contribution of this paper is to show empirically that distributed negotiation mechanisms such as MURDOCH are viable and effective for coordinating physical multirobot systems.

Composition of Web services has received much interest to support business-to-business or enterprise application integration. On the one side, the business world has developed a number of XML-based standards to formalize the specification of Web services, their flow composition and execution. This approach is primarily syntactical: Web service interfaces are like remote procedure call and the interaction protocols are manually written. On the other side, the Semantic Web community focuses on reasoning about web resources by explicitly declaring their preconditions and effects with terms precisely defined in ontologies. For the composition of Web services, they draw on the goal-oriented inferencing from planning. So far, both approaches have been developed rather independently from each other. We compare these approaches...

This manual describes the syntax of PDDL, the Planning Domain Definition Language, the problem-specification language for the AIPS-98 planning competition. The language has roughly the the expressiveness of Pednault's ADL [10] for propositions, and roughly the expressiveness of UMCP [6] for actions. Our hope is to encourage empirical evaluation of planner performance, and development of standard sets of problems all in comparable notations.

One of the more controversial recent planning algorithms is the SHOP algorithm, an HTN planning algorithm that plans for tasks in the same order that they are to be executed. SHOP can use domaindependent knowledge to generate plans very quickly, but it can be difficult to write good knowledge bases for SHOP. Our hypothesis is that this difficulty is because SHOP’s total-ordering requirement for the subtasks of its methods is more restrictive than it needs to be. To examine this hypothesis, we have developed a new HTN planning algorithm called SHOP2. Like SHOP, SHOP2 is sound and complete, and it constructs plans in the same order that they will later be executed. But unlike SHOP, SHOP2 allows the subtasks of each

This paper postulates a rigorous method for the construction of classical planning domain models. We describe, with the help of a non-trivial example, a tool supported method for encoding such models. The method results in an `object-centred' specification of the domain that lifts the representation from the level of the literal to the level of the object. Thus, for example, operators are defined in terms of how they change the state of objects, and planning states are defined as amalgams of the objects' states. The method features two classes of tools: for initial capture and validation of the domain model; and for operationalising the domain model (a process we call compilation) for later planning. Here we focus on compilation tools used to generate macros and goal orders to be utilised at plan generation time. We describe them in depth, and evaluate empirically their combined benefits in plan-generation speed-up. The method's main benefit is in helping the modeller to pro...

A computer program that uses AI planning techniques is now the world's best program for the game of contract bridge. As reported in The New York Times and The Washington Post, this program---a new version of Great Game Products' Bridge Baron program---won the Baron Barclay World Bridge Computer Challenge, an international competition hosted in July 1997 by the American Contract Bridge League. It is well known that the game-tree search techniques used in computer programs for games such as chess and checkers work quite differently from how humans think about such games. In contrast, our new version of the Bridge Baron emulates the way in which a human might plan declarer play in bridge, by using an adaptation of Hierarchical Task Network (HTN) planning. This article gives an overview of the planning techniques that we have incorporated into the Bridge Baron, and discusses what the program's victory signifies for research on AI planning and game-playing.

In the pursuit of an efficient cooperative multi-robot system, the researcher must eventually answer the question "how should robots communicate?"; a natural way to attack this question is to decompose it into three simpler corollaries: "what should robots communicate?", "when should they communicate?" and "with whom should they communicate?". In this paper, we propose answers to these questions in the form of a general framework for inter-robot communication and, more specifically, advocate its use in dynamic task allocation for teams of cooperative mobile robots. We base our communication model on publish/subscribe messaging and validate our system by using it in a tightly-coupled multi-robot manipulation task and a loosely-coupled long-term experiment involving many robots concurrently executing different tasks.

We are interested in solving real-world planning problems and, to that end, argue for the use of domain knowledge in planning. We believe that the field must develop methods capable of using rich knowledge models in order to make planning tools useful for complex problems. We discuss the suitability of current planning paradigms for solving these problems. In particular, we compare knowledge-rich approaches such as hierarchical task network (HTN) planning to minimal-knowledge methods such as STRIPS-based planners and disjunctive planners (DPs). We argue that the former methods have advantages such as scalability, expressiveness, continuous plan modification during execution, and the ability to interact with humans. However, these planners also have limitations, such as requiring complete domain models and failing to model uncertainty, that often make them inadequate for real-world problems. In this paper, we define the terms knowledge-based (KB) and primitive-action (PA) planning, and argue for the use of KB planning as a paradigm for solving real-world problems. We next summarize some of the characteristics of real-world problems that we are interested in addressing. Several current real-world planning applications are described, focusing on the ways in which knowledge is brought to bear on the planning problem. We describe some existing KB approaches, and then discuss additional capabilities, beyond those available in existing systems, that are needed. Finally, we draw an analogy from the current focus of the planning community on disjunctive planners to the experiences of the machine learning community over the past decade.

Much recent planning research has focused on two related issues. First, there has been a strong interest in information-gathering (or "sensing", or "knowledge-producing ") actions. Second, has been an investigation of plans with sophisticated control structures, such as conditional branches and loops. But the combination of these two lines of research poses a representational problem: plans with information-gathering actions that can be executed more than once can have complex information-flow and control-flow relationships. In this paper, we present a framework for the representation and execution of hierarchical plans with information producing actions, conditional branches, periodic actions, and loops. Our framework subsumes several techniques found in the recent literature.

Computationally tractable planning problems reported in the literature so far have almost exclusively been defined by syntactical restrictions. To better exploit the inherent structure in problems, it is probably necessary to study also structural restrictions on the underlying state-transition graph. The exponential size of this graph, though, makes such restrictions costly to test. Hence, we propose an intermediate approach, using a state variable model for planning and defining restrictions on the separate state-transition graphs for each state variable. We identify such restrictions which can tractably be tested and we present a planning algorithm which is correct and runs in polynomial time under these restrictions. The algorithm has been implemented an it outperforms Graphplan on a number of test instances. In addition, we present an exhaustive map of the complexity results for planning under all combinations of four previously studied syntactical restrictions and our five new structural restrictions. This complexity map considers both the optimal and non-optimal plan generation problem.