On the Parallelization of Greedy Regression Tables (original) (raw)

Parallel planning via the distribution of operators

Journal of Experimental & Theoretical Artificial Intelligence, 2001

This paper describes ODMP (Operator Distribution Method for Parallel Planning), a parallelization method for efficient heuristic planning. The method innovates in that it parallelizes the application of the available operators to the current state and the evaluation of the successor states using the heuristic function. In order to achieve better load balancing and a lift in the scalability of the algorithm, the operator set is initially enlarged, by grounding the first argument of each operator. Additional load balancing is achieved through the reordering of the operator set, based on the expected amount of imposed work. ODMP is effective for heuristic planners, but it can be applied to planners that embody other search strategies as well. It has been applied to GRT, a domain-independent heuristic planner, and CL, a heuristic planner for simple Logistics problems, and has been thoroughly tested on a set of Logistics problems adopted from the AIPS-98 planning competition, giving quite promising results.

AI Planning for Transportation Logistics

2001

Abstract: In the last decade the efficiency of the Artificial Intelligence Planning Systems has been increased significantly. New systems appeared that are able to cope with planning problems being orders of magnitude more complex than the ones solvable in early 90's. This vast improvement increase was made possible mainly by three new approaches in plan generation: planning graphs, satisfiability planning and heuristic state-space planning.

Tactical Planning in the Fast Moving Consumer Goods Industry: An SKU Decomposition Algorithm

2018

Tactical planning models for the Fast Moving Consumer Goods (FMCG) industry can quickly become intractable due to the extremely large number of Stock Keeping Units (SKUs). We propose an SKU decomposition algorithm that is aimed at being able to solve cases containing thousands of SKUs. The full tactical planning model is decomposed into a set of single SKU models. These models are then solved sequentially. The capacity used by other SKUs is removed from the available capacity and, at a certain penalty cost, a violation of the capacity is initially allowed. By slowly increasing the penalty cost, the capacity violations are decreased until a feasible solution is obtained. Using the algorithm it was possible to obtain solutions within a few percent of optimality for example cases containing 10 and 25 SKUs. It was also possible to solve a larger 100 SKU case for which the full space model was intractable. The main advantage of the algorithm is that the required CPU time scales approxima...

Using Tabled Logic Programming to Solve the Petrobras Planning Problem

Theory and Practice of Logic Programming, 2014

Tabling has been used for some time to improve efficiency of Prolog programs by memorizing answered queries. The same idea can be naturally used to memorize visited states during search for planning. In this paper we present a planner developed in the Picat language to solve the Petrobras planning problem. Picat is a novel Prolog-like language that provides pattern matching, deterministic and non-deterministic rules, and tabling as its core modelling and solving features. We demonstrate these capabilities using the Petrobras problem, where the goal is to plan transport of cargo items from ports to platforms using vessels with limited capacity. Monte Carlo Tree Search has been so far the best technique to tackle this problem and we will show that by using tabling we can achieve much better runtime efficiency and better plan quality.

Accelerating Partial-Order Planners: Some Techniques for E ective Search Control and Pruning

We propose some domain-independent techniques for bringing well-founded partialorder planners closer to practicality. The rst two techniques are aimed at improving search control while keeping overhead costs low. One is based on a simple adjustment to the default A* heuristic used by ucpop to select plans for re nement. The other is based on preferring \zero commitment" (forced) plan re nements whenever possible, and using LIFO prioritization otherwise. A more radical technique is the use of operator parameter domains to prune search. These domains are initially computed from the de nitions of the operators and the initial and goal conditions, using a polynomial-time algorithm that propagates sets of constants through the operator graph, starting in the initial conditions. During planning, parameter domains can be used to prune nonviable operator instances and to remove spurious clobbering threats. In experiments based on modi cations of ucpop, our improved plan and goal selection strategies gave speedups by factors ranging from 5 to more than 1000 for a variety of problems that are nontrivial for the unmodi ed version. Crucially, the hardest problems gave the greatest improvements. The pruning technique based on parameter domains often gave speedups by an order of magnitude or more for di cult problems, both with the default ucpop search strategy and with our improved strategy. The Lisp code for our techniques and for the test problems is provided in on-line appendices.

Intelligent Optimization for Logistics

Optimization has been a remarkable problem solution approach for especially real-world based cases. Since first use of classical optimization techniques, many different fields in the modern life have benefited from them. But after a while, more advanced optimization problems required use of more effective techniques. At this point, Computer Science took an important role on providing software related techniques to improve the associated literature. Today, Artificial Intelligence based intelligent optimization techniques are widely used within optimization problems. Objective of this paper is to provide a brief look at to the use of intelligent optimization in Logistics field. It is clearly known that Logistics operations and managerial aspects of this field often deal with optimization based problems. So, it is thought that use of alternative, recent optimization techniques may give a light to further optimization problem solutions for the Logistics field. In this context, the authors believe that this paper will be an interesting reference for the literature of both Computer Science and Logistics.

Planning models for freight transportation

European Journal of Operational Research, 1997

The objective of this paper is to identify some of the main issues in freight transportation planning and operations, and to present appropriate Operations Research models and methods, as well as computer-based planning tools. The presentation is organized according to the three classical decision-making levels: strategic, tactic, operational. For each case, the problem and main issues are described, followed by a brief literature review and significant methodological and instrumental developments. We conclude with a few development perspectives. (~) 1997 Elsevier Science B.V.

AltAltp: Online parallelization of plans with heuristic state search

2003

Despite their near dominance, heuristic state search planners still lag behind disjunctive planners in the generation of parallel plans in classical planning. The reason is that directly searching for parallel solutions in state space planners would require the planners to branch on all possible subsets of parallel actions, thus increasing the branching factor exponentially. We present a variant of our heuristic state search planner AltAlt called AltAlt p which generates parallel plans by using greedy online parallelization of partial plans. The greedy approach is significantly informed by the use of novel distance heuristics that AltAlt p derives from a graphplan-style planning graph for the problem. While this approach is not guaranteed to provide optimal parallel plans, empirical results show that AltAlt p is capable of generating good quality parallel plans at a fraction of the cost incurred by the disjunctive planners.

SAT-Based Parallel Planning Using a Split Representation of Actions

2009

Planning based on propositional SAT(isfiability) is a powerful approach to computing step-optimal plans given a parallel execution semantics. In this setting: (i) a solution plan must be minimal in the number of plan steps required, and (ii) non-conflicting actions can be executed instantaneously in parallel at a plan step. Underlying SAT-based approaches is the invocation of a decision procedure on a SAT encoding of a bounded version of the problem. A fundamental limitation of existing approaches is the size of these encodings. This problem stems from the use of a direct representation of actions -i.e. each action has a corresponding variable in the encoding. A longtime goal in planning has been to mitigate this limitation by developing a more compact split -also termed lifted -representation of actions in SAT encodings of parallel step-optimal problems. This paper describes such a representation. In particular, each action and each parallel execution of actions is represented uniquely as a conjunct of variables. Here, each variable is derived from action pre and post-conditions. Because multiple actions share conditions, our encoding of the planning constraints is factored and relatively compact. We find experimentally that our encoding yields a much more efficient and scalable planning procedure over the state-of-the-art in a large set of planning benchmarks.