In most real-world reasoning problems, planning and scheduling phases are loosely coupled. For example, in project planning, the user comes up with a task list and schedules it with a scheduling tool like Microsoft Project. One can view automated planning in a similar way in which there is an action selection phase where actions are selected and ordered to reach the desired goals, and a resource allocation phase where enough resources are assigned to ensure the successful execution of the chosen actions. On the other hand, most existing automated planners studied in Artificial Intelligence do not exploit this loose-coupling and perform both action selection and resource assignment employing the same algorithm. The current work shows that the above strategy severely curtails the scale-up potential of existing state of the art planners which can be overcome by leveraging the loose coupling. Specifically, a novel planning framework called RealPlan is developed in which resource allocatio...

Although the deep affinity between Graphplan's backward search, and the process of solving constraint satisfaction problems has been noted earlier, these relations have hither-to been primarily used to adapt CSP search techniques into the backward search phase of Graphplan. This paper describes GP-CSP, a system that does planning by automatically converting Graphplan's planning graph into a CSP encoding, and solving the CSP encoding using standard CSP solvers. Our comprehensive empirical evaluation of GP-CSP demonstrates that it is quite competitive with both standard Graphplan and Blackbox system, which compiles planning graphs into SAT encodings. We discuss the many advantages offered by focusing on CSP encodings rather than SAT encodings, including the fact that by exploiting implicit constraint representations, GP-CSP tends to be less susceptible to memory blow-up associated with methods that compile planning problems into SAT encodings. Our work is inspired by t...

Component-based approaches are becoming increasingly popular in the areas of adaptive distributed systems, web services, and grid computing. In each case, the underlying infrastructure needs to address a deployment problem involving the placement of application components onto computational, data, and network resources across a wide-area environment subject to a variety of qualitative and quantitative constraints. In general, the deployment needs to also introduce auxiliary components (e.g., to compress/decompress data, or invoke GridFTP sessions to make data available at a remote site), and reuse preexisting components and data. To provide the flexibility required in the latter case, recently proposed systems such as Sekitei and Pegasus have proposed solutions that rely upon AI planning-based techniques. Although promising, the inherent complexity of AI planning and the fact that constraints governing component deployment often involve non-linear and non-reversible functions have prevented such solutions from generating deployments in resource-constrained situations and achieving optimality in terms of overall resource usage or other cost metrics. This paper addresses both of these shortcomings in the context of the Sekitei system. Our extension relies upon information supplied by a domain expert, which classifies component behavior into a discrete set of levels. This discretization, often justified in practice, permits the planner to identify cost-optimal plans (whose quality improves with the level definitions) without restricting the form of the constraint functions. We describe the modified Sekitei algorithm, and characterize, using a media stream delivery application, its scaling behavior when generating optimal deployments for various network configurations. 1

The need to eciently allocate resources to the processors responsible for performing actions in a plan is generally not emphasised in the design of planning domains or in the quality metrics that determine the value of a plan. Although some researchers have considered problems involving resources, there are few planning systems capable of solving problems in which their allocation is a key aspect. Planners that do address this problem rely on the explicit identi- cation of resources in the domain description, and resource conict resolution to keep the allocation of resources to within allowable bounds. This approach does not assist the planner in determining how to tackle the resource allocation part of the problem eciently. In this paper we describe how domain analysis can be used to recognise the occurrence of a common resource allocation sub-problem within planning domains. STAN4, a hybrid system that uses domain analysis to identify appropriate heuristic stra...

This paper is intended to identify the nature of resources and the roles they can play in planning problems, and to consider ways in which planning technology might be extended to handle them.

In this paper we present a classification scheme which circumscribes a large class of resources found in the real world. Building on the work of others we also define key properties of resources that allow formal expression of the proposed classification. Furthermore, operations that change the state of a resource are formalized. Together, properties and operations go a long way in formalizing the representation and reasoning aspects of resources for planning.

Abstract. Reformulation of a problem is intended to make the problem more amenable to efficient solution. This is equally true in the special case of reformulating a planning problem. This paper considers various ways in which reformulation can be exploited in planning. 1

Planning is traditionally associated with the search for sequences of actions spread over time. In this paper we present a component placement problem (CPP), which is concerned with sequences of actions spread in the space of wide-area networks. We argue that, despite the lack of a time component, the CPP is a planning problem. We discuss complexity of the CPP and similarities between the CPP and traditional AI planning. 1

Abstract – Models of plan formation by teams of autonomous, social agents are of interest in the application of multi-agent systems in both engineering and commerce. A team of agents may be motivated by the need to achieve a common goal, and must agree on a plan of action. However, being autonomous entities, they may differ in the contributions that they are capable of making and may have varying attitudes towards the options available (e.g. preferences over who should do what). Furthermore, in general, it is not appropriate for a team plan to be imposed by a single manager agent. In this paper we investigate methods of collaborative plan construction and conflict resolution in teams of autonomous, social agents. We propose a multi-agent planning algorithm that interleaves planning with information exchange, coordination and negotiation, allowing the agents to handle potential conflicts, and to promote their own preferences over the derived solutions. Keywords: Planning, Agents. 1

Abstract. This paper investigates a loosely coupled approach to planning and scheduling integration, which consists of cascading a planner and a scheduler. While other implementations of this framework have already been reported, our work aims at analyzing the structural properties of the scheduling problem which results from the planning component, focusing on the bias produced by different planning approaches in the light of makespan-optimizing scheduling. 1