Álvaro Torralba

Distance? Who Cares? tailoring merge-and-shrink heuristics to detect unsolvability

Research on heuristic functions is all about estimating the length (or cost) of solution paths. But what if there is no such path? Many known heuristics have the ability to detect (some) unsolvable states, but that ability has always been treated as a by-product. No attempt has been made to design heuristics specifically for that purpose, where there is no need to preserve distances. As a case study towards leveraging that advantage, we investigate merge-and-shrink abstractions in classical planning. We identify safe abstraction steps (no information loss regarding solvability) that would not be safe for traditional heuristics. We design practical algorithm configurations, and run extensive experiments showing that our heuristics outperform the state of the art for proving planning tasks unsolvable.

Combining linear programming and automated planning to solve intermodal transportation problems

When dealing with transportation problems Operational Research (OR), and related areas as Artificial Intelligence (AI), have focused mostly on uni-modal transport problems. Due to the current existence of bigger international logistics companies, transportation problems are becoming increasingly more complex. One of the complexities arises from the use of intermodal transportation. Intermodal transportation reflects the combination of at least two modes of transport in a single transport chain, without a change of container for the goods. In this paper, a new hybrid approach is described which addresses complex intermodal transport problems. It combines OR techniques with AI search methods in order to obtain good quality solutions, by exploiting the benefits of both kinds of techniques. The solution has been applied to a real world problem from one of the largest spanish companies using intermodal transportation, Acciona Transmediterranea Cargo.

Symbolic A* search with pattern databases and the merge-and-shrink abstraction

The efficiency of heuristic search planning crucially depends on the quality of the search heuristic, while succinct representations of state sets in decision diagrams can save large amounts of memory in the exploration. BDDA* - a symbolic version of A* search - combines the two approaches into one algorithm. This paper compares two of the leading heuristics for sequential-optimal planning: the merge-and-shrink and the pattern databases heuristic, both of which can be compiled into a vector of BDDs and be used in BDDA*. The impact of optimizing the variable ordering is highlighted and experiments on benchmark domains are reported.

Search Challenges in Natural Language Generation with Complex Optimization Objectives

AbstractAutomatic natural language generation (NLG) is a difficult problem already when merely trying to come up with natural-sounding utterances. Ubiquituous applications, in particular companion technologies, pose the additional challenge of flexible adaptation to a user or a situation. This requires optimizing complex objectives such as information density, in combinatorial search spaces described using declarative input languages. We believe that AI search and planning is a natural match for these problems, and could substantially contribute to solving them effectively. We illustrate this using a concrete example NLG framework, give a summary of the relevant optimization objectives, and provide an initial list of research challenges.

Lex-Partitioning: A New Option for BDD Search

For the exploration of large state spaces, symbolic search using binary decision diagrams (BDDs) can save huge amounts of memory and computation time. State sets are represented and modified by accessing and manipulating their characteristic functions. BDD partitioning is used to compute the image as the disjunction of smaller subimages. In this paper, we propose a novel BDD partitioning option. The partitioning is lexicographical in the binary representation of the states contained in the set that is represented by a BDD and uniform with respect to the number of states represented. The motivation of controlling the state set sizes in the partitioning is to eventually bridge the gap between explicit and symbolic search. Let n be the size of the binary state vector. We propose an O(n) ranking and unranking scheme that supports negated edges and operates on top of precomputed satcount values. For the uniform split of a BDD, we then use unranking to provide paths along which we partition the BDDs. In a shared BDD representation the efforts are O(n). The algorithms are fully integrated in the CUDD library and evaluated in strongly solving general game playing benchmarks.

Efficient symbolic search for cost-optimal planning

In cost-optimal planning we aim to find a sequence of operators that achieve a set of goals with minimum cost. Symbolic search with Binary Decision Diagrams (BDDs) performs efficient state space exploration in terms of time and memory. This is crucial in optimal settings, in which large parts of the state space must be explored in order to prove optimality. However, the development of accurate heuristics for explicit-state search in recent years have left symbolic search techniques in a secondary place.In this article we propose two orthogonal improvements for symbolic search planning. On the one hand, we analyze and compare different methods for image computation in order to efficiently perform the successor generation on symbolic search. Image computation is the main bottleneck of symbolic search algorithms so an efficient computation is paramount for efficient symbolic search planning. On the other hand, we study how to use state-invariant constraints to prune states in symbolic search. This is essential in regression search but it is yet to be exploited in symbolic search planners.Experiments with symbolic bidirectional uniform-cost search and symbolic A * search with PDBs show remarkable performance improvements on most IPC benchmark domains. Overall, with the help of our improvements, symbolic bidirectional search outperforms explicit-state search with state-of-the-art heuristics such as LM-cut across many different domains.

On creating complementary pattern databases

A pattern database (PDB) for a planning task is a heuristic function in the form of a lookup table that contains optimal solution costs of a simplified version of the task. In this paper we introduce a method that sequentially creates multiple PDBs which are later combined into a single heuristic function. At a given iteration, our method uses estimates of the A* running time to create a PDB that complements the strengths of the PDBs created in previous iterations. We evaluate our algorithm using explicit and symbolic PDBs. Our results show that the heuristics produced by our approach are able to outperform existing schemes, and that our method is able to create PDBs that complement the strengths of other existing heuristics such as a symbolic perimeter heuristic.

TIMIPLAN: A Tool for Transportation Tasks

Multi-modal transportation is a logistics problem in which a set of goods has to be transported to different places, with the combination of at least two modes of transport, without a change of container for the goods. In such tasks, in many cases, the decisions are inefficiently made by human operators. Human operators receive plenty of information from several and varied sources, and thus they suffer from information overload. To solve efficiently the multi-modal transportation problem, the management cannot rely only on the experience of the human operators. A prospective way to tackle the complexity of the problem for multi-modal transportation is to apply the concept of autonomic behavior. The goal of this chapter is to describe timiplan, a software tool that solves multi-modal transportation problems developed in cooperation with the Spanish company Acciona Transmediterranea. The tool includes a solver that combines linear programming (LP) with automated planning (AP) techniques. To facilitate its integration in the company, the application follows a mixed-initiative approach allowing the users to modify the plans provided by the planning module. The system also integrates an execution component that monitors the execution, keeping track of failures and replanning if necessary. Thus, timiplan showcases some of the needed autonomic objectives for self-management in future software applied to road transport software system.

Symbolic perimeter abstraction heuristics for cost-optimal planning

Abstract In the context of heuristic search within automated planning, abstraction heuristics map the problem into an abstract instance and use the optimal solution cost in the abstract state space as an estimate for the real solution cost. Their flexibility in choosing different abstract mappings makes abstractions a powerful tool to obtain domain-independent heuristics. Different types of abstraction heuristics exist depending on how the mapping is defined, like Pattern Databases (PDBs) or Merge-and-Shrink (M&S). In this paper, we consider two variants of PDBs, symbolic and perimeter PDBs, combining them to take advantage of their synergy. Symbolic PDBs use decision diagrams in order to efficiently traverse the abstract state space. Perimeter PDBs derive more informed estimates by first constructing a perimeter around the goal and then using it to initialize the abstract search. We generalize this idea by considering a hierarchy of abstractions. Our algorithm starts by constructing a symbolic perimeter around the goal and, whenever continuing the search becomes unfeasible, it switches to a more abstracted state space. By delaying the use of an abstraction, the algorithm derives heuristics as informed as possible. Moreover, we prove that M&S abstractions with a linear merge strategy can be efficiently represented as decision diagrams, enabling the use of symbolic search with M&S abstractions as well as with PDBs. Our experimental evaluation shows that symbolic perimeter abstractions are competitive with other state-of-the-art heuristics.

From Qualitative to Quantitative Dominance Pruning for Optimal Planning.

Dominance relations compare states to determine whether one is at least as good as another in terms of their goal distance. We generalize these qualitative yes/no relations to functions that measure by how much a state is better than another. This allows us to distinguish cases where the state is strictly closer to the goal. Moreover, we may obtain a bound on the difference in goal distance between two states even if there is no qualitative dominance. We analyze the multiple advantages that quantitative dominance has, like discovering coarser dominance relations, or trading dominance by g-value. Moreover, quantitative dominance can also be used to prove that an action starts an optimal plan from a given state. We introduce a novel action selection pruning that uses this to prune any other successor. Results show that quantitative dominance pruning greatly reduces the search space, significantly increasing the planners’ performance.