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...

Introduction We describe the GPT system and its utilization over a number of examples. GPT (General Planning Tool) is an integrated software tool for modeling, analyzing and solving a wide range of planning problems dealing with uncertainty and partial information, that has been used for us and others for research and teaching. Our approach is based on different state models that can handle various types of action dynamics (deterministic and probabilistic) and sensor feedback (null, partial, and complete). The system consists mainly of a high-level language for expressing actions, sensors, and goals, and a bundle algorithms based on heuristic search for solving them. The language is one of GPT's strengths since it presents the user a consistent and unified framework for the planning task. These descriptions are then solved by appropriate algorithms chosen from the bun

In this paper we are concerned with the problem of learning how to solve planning problems in one domain given a number of solved instances. This problem is formulated as the problem of inferring a function that operates over all instances in the domain and maps states and goals into actions. We call such functions generalized policies and the question that we address is how to learn suitable representations of generalized policies from data. This question has been addressed recently by Roni Khardon [16]. Khardon represents generalized policies using an ordered list of existentially quantified rules that are inferred from a training set using a version of Rivest's learning algorithm [22]. Here, we follow Khardon's approach but represent generalized policies in a different way using a concept language. We show through a number of experiments in the blocks-world that the concept language yields a better policy using a smaller set of examples and no background knowle...

The TM-LPSAT planner can construct plans in domains containing atomic actions and durative actions; events and processes; discrete, real-valued, and interval-valued fluents; and continuous linear change to quantities. It works in three stages. In the first stage, a representation of the domain and problem in an extended version of PDDL+ is compiled into a system of propositional combinations of propositional variables and linear constraints over numeric variables. In the second stage, the LPSAT constraint engine (Wolfman & Weld 2000) is used to find a solution to the system of constraints. In the third stage, a correct parallel plan is extracted from this solution. We discuss the structure of the planner and show how a real-time temporal model is compiled into LPSAT constraints.

Effective planning requires good modeling languages and good algorithms. The Strips language has shaped most of the work in planning since the early 70's due to its effective solution of the frame problem and its support for divide-and-conquer strategies. In recent years, however, planning strategies not based on divide-and-conquer and work on theories of actions suggest that alternative languages can make modeling and planning easier. With this goal in mind, we have developed Functional Strips, a language that adds first-class function symbols to Strips providing additional flexibility in the codification of planning problems. This extension is orthogonal and complementary to extensions accommodated in other languages such as conditional effects, quantification, negation, etc. Function symbols, unlike relational symbols, can be nested so objects need not be referred to by their explicit names and as a result more efficient encodings can be provided. For example, a problem like the 8-p...

The model used in this report focuses on the analysis of ship waiting statistics and stock fluctuations under different arrival processes. However, the basic outline is the same: central to both models are a jetty and accompanying tankfarm facilities belonging to a new chemical plant in the Port of Rotterdam. Both the supply of raw materials and the export of finished products occur through ships loading and unloading at the jetty. Since disruptions in the plants production process are very expensive, buffer stock is needed to allow for variations in ship arrivals and overseas exports through large ships. Ports provide jetty facilities for ships to load and unload their cargo. Since ship delays are costly, terminal operators attempt to minimize their number and duration. Here, simulation has proved to be a very suitable tool. However, in port simulation models, the impact of the arrival process of ships on the model outcomes tends to be underestimated. This article considers three arrival processes: stock-controlled, equidistant per ship type, and Poisson. We assess how their deployment in a port simulation model, based on data from a real case study, affects the efficiency of the loading and unloading process. Poisson, which is the chosen arrival process in many client-oriented simulations, actually performs worst in terms of both ship delays and required storage capacity. Stock-controlled arrivals perform best with regard to ship delays and required storage capacity. In the case study two types of arrival processes were considered. The first type are the so-called stock-controlled arrivals, i.e., ship arrivals are scheduled in such a way, that a base stock level is maintained in the tanks. Given a base stock level of a raw material or ...