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Conformant planning is the problem of finding a sequence of actions that is guaranteed to achieve the goal for any possible initial state and nondeterministic behavior of the planning domain. In this paper we present a new approach to conformant planning. We propose an algorithm that returns the set of all conformant plans of minimal length if the problem admits a solution, otherwise it returns with...
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...
This paper explores the performance of three planners, viz. parcPLAN, IPP and Blackbox, on a variant of the standard blocks-world problem. The variant problem has a restricted number of table positions, and the number of arms can vary (from 1 upwards). This type of problem is typical of many real world planning problems, where resources form a significant component. The empirical studies reveal that...
To date, no one planner has demonstrated clearly superior performance. Although researchers have hypothesized that this should be the case, no one has performed a large study to test its limits. In this research, we tested performance of a set of planners to determine which is best on what types of problems. The study included six planners and over 200 problems. We found that performance, as measured...
Most exact algorithms for solving partially observable Markov decision processes (POMDPs) are based on a form of dynamic programming in which a piecewise-linear and convex representation of the value function is updated at every iteration to more accurately approximate the true value function. However, the process is computationally expensive, thus limiting the practical application of POMDPs in planning...
Planning as satisfiability has hitherto focused only on purely generative planning. There is an evidence in traditional refinement planning that planning incrementally by reusing or merging plans can be more efficient than planning from scratch (sometimes reuse is not more efficient, but becomes necessary if the cost of abandoning the reusable plan is too high, when users are charged for the planning...
In this paper we study the consistency problem for a set of disjunctive temporal constraints [Stergiou and Koubarakis, 1998]. We propose two SAT-based procedures, and show that—on sets of binary randomly generated disjunctive constraints—they perform up to 2 orders of magnitude less consistency checks than the best procedure presented in [Stergiou and Koubarakis, 1998]. On these tests, our experimental...
We extend a planning algorithm to cover simple forms of arithmetics. The operator preconditions can refer to the values of numeric variables and the operator postconditions can modify the values of numeric variables. The basis planning algorithm is based on techniques from propositional satisfiability testing and does not restrict to forward or backward chaining. When several operations affect a numeric...
The satisfiability paradigm has been hitherto applied to planning with only primitive actions. On the other hand, hierarchical task networks have been successfully used in many real world planning applications. Adapting the satisfiability paradigm to hierarchical task network planning, we show how the guidance from the task networks can be used to significantly reduce the sizes of the propositional...
In this paper we present a general-purposed algorithm for transforming a planning problem specified in Strips into a concise state description for single state or symbolic exploration. The process of finding a state description consists of four phases. In the first phase we symbolically analyze the domain specification to determine constant and one-way predicates, i.e. predicates that remain...
Recent progress in the applications of propositional planning systems has led to an impressive speed-up of solution time and an increase in tractable problem size. In part, this improvement stems from the use of domain-dependent knowledge in form of state constraints. In this paper we introduce a different class of constraints: action constraints . They express domain-dependent knowledge about the...
Constraint Satisfaction techniques have been recognized to be effective tools for increasing the efficiency of least commitment planners. We focus on least commitment on variable binding. A constraint based approach for this issue has been previously proposed by Yang and Chan [21]. In this setting, the planning problem is mapped onto a Constraint Satisfaction Problem. Its variables represent domain...
Planning consists of 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. In most real-world problems, these two phases are loosely coupled. Most existing planners do not exploit this loose-coupling, and perform both action selection...
In a call center, service agents with different capabilities are available for solving incoming customer problems at any time. To supply quick response and better problem solution to customers, it is necessary to schedule customer problems to appropriate service agents efficiently. We developed SANet, a service agent network for call center, which integrates multiple service agents including both...
This paper describes the development of an intelligent tasking model which has been designed to enable complex systems, human agents and software agents, to be tasked and controlled within a reactive work ow management paradigm. The task models exploit recent advances within the AI community in reactive control, scheduling and continuous execution. The Dynamic Execution Order Scheduler ( DEOS ) extends...
This paper investigates the performance of a set of greedy algorithms for solving the Multi-Capacitated Metric Scheduling Problem (MCM-SP). All algorithms considered are variants of ESTA (Earliest Start Time Algorithm), previously proposed in [3]. The paper starts with an analysis of ESTA’s performance on different classes of MCM-SP problems. ESTA is shown to be effective on several of these classes,...
We study an automata-theoretic approach to planning for temporally extended goals. Specifically, we devise techniques based on nonemptiness of Büchi automata on infinite words, to synthesize sequential and conditional plans in a generalized setting in which we have that: goals are general temporal properties of desired execution; dynamic systems are represented by finite transition systems; incomplete...
Part of the recent work in AI planning is concerned with the development of algorithms that regard planning as a combinatorial search problem. The underlying representation language is basically propositional logic. While this is adequate for many domains, it is not clear if it remains so for problems that involve numerical constraints, or optimization of complex objective functions. Moreover, the...
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