The formulation of planning as heuristic search with heuristics derived from problem representations has turned out to be a fruitful approach for classical planning. In this paper, we pursue a similar idea in the context planning with incomplete information. Planning with incomplete information can be formulated as a problem of search in belief space, where belief states can be either sets of states or more generally probability distribution over states. While the formulation (as the formulation of classical planning as heuristic search) is not particularly novel, the contribution of this paper is to make it explicit, to test it over a number of domains, and to extend it to tasks like planning with sensing where the standard search algorithms do not apply. The resulting planner appears to be competitive with the most recent conformant and contingent planners (e.g., cgp, sgp, and cmbp) while at the same time is more general as it can handle probabilistic actions and se...

Different planning techniques have been proposed so far which address the problem of automated composition of web services. However, in realistic cases, the planning problem is far from trivial: the planner needs to deal with the nondeterministic behaviour of web services, the partial observability of their internal status, and with complex goals, e.g., expressing temporal conditions and preference requirements. We propose...

This paper describes a new symbolic model checker, called NUSMV, developed as part of a joint project between CMU and IRST. NUSMV is the result of the reengineering, reimplementation, and, to a limited extent, extension of the CMU SMV model checker. The core of this paper consists of a detailed description of the NUSMV functionalities, architecture, and implementation.

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...

agent or group of agents. In this paper, we address the problem of how plans might be developed for a group of agents to cooperate to bring about such a goal. We present a novel approach to this problem, in which the problem is formulated as one of model checking in Alternating Temporal Epistemic Logic (ATEL). After introducing this logic, we present a model checking algorithm for it, and show that the model checking problem for this logic is tractable. We then show how multiagent planning can be treated as a model checking problem in ATEL, and discuss the related issue of checking knowledge preconditions for multiagent plans. We illustrate the approach with an example. We then describe how this example was implemented using the MOCHA model checking system, and conclude by discussing the relationship of our work with that of others in the planning and speech acts communities.

The Model Based Planner (MBP) is a system for planning in non-deterministic domains. It can generate plans automatically to solve various planning problems, like conformant planning, planning under partial observability, and planning for temporally extended goals. Moreover, MBP can validate plans, and offers a variety of simulation functionalities for plans and domains. MBP is based on Symbolic Model Checking techniques, and Binary Decision Diagrams (BDDs), that provide a practical solution to the problem of dealing with the large size of realistic planning problems. Experimental analysis in the course of the last years has shown MBP to be state-of-the-art in planning for nondeterministic domains. The demo aims at showing MBP’s array of functionalities for plan generation, validation and simulation over an increasingly complex navigation problem.

The Model Checking Integrated Planning System (MIPS) has shown distinguished performance in the second and third international planning competitions. With its object-oriented framework architecture MIPS clearly separates the portfolio of explicit and symbolic heuristic search exploration algorithms from different on-line and off-line computed estimates and from the grounded planning problem representation.

This article gives an overview of AI (Artificial Intelligence) plan-ning techniques and discusses their application to the Web service composition problem.

Several realistic non-deterministic planning domains require plans that encode iterative trial-and-error strategies, e.g., "pick up a block until succeed". In such domains, a certain effect (e.g., action success) might never be guaranteed a priori of execution and, in principle, iterative plans might loop forever. Here, the planner should generate iterative plans whose executions always have a possibility of terminating and, when they do, they are guaranteed to achieve the goal. In this paper, we define the notion of strong cyclic plan, which formalizes in temporal logic the above informal requirements for iterative plans, define a planning algorithm based on model-checking techniques, and prove that the algorithm is guaranteed to return strong cyclic plans when they exist or to terminate with failure when they do not. We show how this approach can be extended to formalize plans that are guaranteed to achieve the goal and do not involve iterations (strong plans) and plans that have a possibility (but are not guaranteed) to achieve the goal (weak plans). The results presented in this paper constitute a formal account for "planning via model checking" in non-deterministic domains, which has never been provided before.

Abstract — Robot motion planning algorithms have focused on low-level reachability goals taking into account robot kinematics, or on high level task planning while ignoring low-level dynamics. In this paper, we present an integrated approach to the design of closed–loop hybrid controllers that guarantee by construction that the resulting continuous robot trajectories satisfy sophisticated specifications expressed in the so–called Linear Temporal Logic. In addition, our framework ensures that the temporal logic specification is satisfied even in the presence of an adversary that may instantaneously reposition the robot within the environment a finite number of times. This is achieved by obtaining a Büchi automaton realization of the temporal logic specification, which supervises a finite family of continuous feedback controllers, ensuring consistency between the discrete plan and the continuous execution. I.