Optimised Variants of Polynomial Compilation for Conditional Effects in Classical Planning

Conditional effects are a key feature in classical planning, enabling the description of actions whose outcomes are statedependent. It is well known that removing conditional effects in a polynomial way necessarily increases the size of a valid plan by a polynomial factor. However, preserving the exact plan size requires encoding the problem exponentially. The paper proposes and empirically evaluates optimisations for existing polynomial compilations. These optimisations aim to make the resulting compilations more suitable for planners while limiting the increase in plan size, which is inevitable if we want to keep the compilation polynomial. Specifically, the paper introduces a polynomial compilation technique that expands conditional effects when their number is below a certain threshold and sequentialises them otherwise. Additionally, the paper demonstrates that even straightforward optimisations can have a notable impact

An Effective Polynomial Technique for Compiling Conditional Effects Away

The paper introduces a novel polynomial compilation technique for the sound and complete removal of conditional effects in classical planning problems. Similar to Nebel’s polynomial compilation of conditional effects, our solution also decomposes each action with conditional effects into several simpler actions. However, it does so more effectively by exploiting the actual structure of the given conditional effects. We characterise such a structure using a directed graph and leverage it to significantly reduce the number of additional atoms required, thereby shortening the size of valid plans. Our experimental analysis indicates that this approach enables the effective use of polynomial compilations, offering benefits in terms of modularity and reusability of existing planners. It also demonstrates that a compilation-based approach can be more efficient, either independently or in synergy with state-of-the-art optimal planners that directly support conditional effects

On Planning with Qualitative State-Trajectory Constraints in PDDL3 by Compiling them Away

We tackle the problem of classical planning with qualitative state-trajectory constraints as those that can be expressed in PDDL3. These kinds of constraints allow a user to formally specify which temporal properties a plan has to conform with through a class of LTL formulae. We study a compilation-based approach that does not resort to automata for representing and dealing with such properties, as other approaches do, and generates a classical planning problem with conditional effects that is solvable iff the original PDDL3 problem is. Our compilation exploits a regression operator to revise the actions’ preconditions and conditional effects in a way to (i) prohibit executions that irreversibly violate temporal constraints (ii) be sensitive to executions that traverse those necessary subgoals implied by the temporal specification. An experimental analysis shows that our approach performs better than other state-of-the-art approaches over the majority of the considered benchmark domains

Width-based search for multi agent privacy-preserving planning

In multi-agent planning, preserving the agents' privacy has become an increasingly popular research topic. For preserving the agents' privacy, agents jointly compute a plan that achieves mutual goals by keeping certain information private to the individual agents. Unfortunately, this can severely restrict the accuracy of the heuristic functions used while searching for solutions. It has been recently shown that, for centralized planning, blind search algorithms such as width-based search can solve instances of many existing domains in low polynomial time when they feature atomic goals. Moreover, the performance of goal-oriented search can be improved by combining it with width-based search. In this paper, we investigate the usage of width-based search in the context of (decentralised) collaborative multi-agent privacy-preserving planning, addressing the challenges related to the agents' privacy and performance. In particular, we show that width-based search is a very effective approach over several benchmark domains, even when the search is driven by heuristics that roughly estimate the distance from goal states, computed without using the private information of other involved agents. Moreover, we show that the use of width-based techniques can significantly reduce the number of messages transmitted among the agents, better preserving their privacy and improving their performance. An experimental study presented in the paper analyses the effectiveness of our techniques, and compares them with the state-of-the-art of collaborative multi-agent planning

Generating and exploiting cost predictions in heuristic state-space planning

This paper proposes and investigates a novel way of combining machine learning and heuristic search to improve domain-independent planning. On the learning side, we use learning to predict the plan cost of a good solution for a given instance. On the planning side, we propose a bound-sensitive heuristic function that exploits such a prediction in a state-space planner. Our function combines the input prediction (derived inductively) with some pieces of information gathered during search (derived deductively). As the prediction can sometimes be grossly inaccurate, the function also provides means to recognise when the provided information is actually misguiding the search. Our experimental analysis demonstrates the usefulness of the proposed approach in a standard heuristic best-first search schema

Best-First Width Search for Multi Agent Privacy-preserving Planning

In multi-agent planning, preserving the agents' privacy has become an increasingly popular research topic. For preserving the agents' privacy, agents jointly compute a plan that achieves mutual goals by keeping certain information private to the individual agents. Unfortunately, this can severely restrict the accuracy of the heuristic functions used while searching for solutions. It has been recently shown that, for centralized planning, the performance of goal oriented search can be improved by combining goal oriented search and width-based search. The combination of these techniques has been called best-first width search. In this paper, we investigate the usage of best-first width search in the context of (decentralised) multi-agent privacy-preserving planning, addressing the challenges related to the agents' privacy and performance. In particular, we show that best-first width search is a very effective approach over several benchmark domains, even when the search is driven by heuristics that roughly estimate the distance from goal states, computed without using the private information of other agents. An experimental study analyses the effectiveness of our techniques and compares them with the state-of-the-art.Comment: Accepted in ICAPS-1