Machetli: Simplifying Input Files for Debugging

Debugging can be a painful task, especially when bugs only occur for large input files. We present Machetli, a tool to help with debugging in such situations. It takes a large input file and cuts away parts of it, while still provoking the bug. The resulting file is much smaller than the original, making the bug easier to find and fix. In our experience, Machetli was able to reduce planning tasks with thousands of actions to trivial tasks that could even be solved by hand. Machetli is an open-source project and it can be extended to other use cases such as debugging SAT solvers or LaTeX compilation bugs

Probing the action of a novel anti-leukaemic drug therapy at the single cell level using modern vibrational spectroscopy techniques

Acute myeloid leukaemia (AML) is a life threatening cancer for which there is an urgent clinical need for novel therapeutic approaches. A redeployed drug combination of bezafibrate and medroxyprogesterone acetate (BaP) has shown anti-leukaemic activity in vitro and in vivo. Elucidation of the BaP mechanism of action is required in order to understand how to maximise the clinical benefit. Attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy, Synchrotron radiation FTIR (S-FTIR) and Raman microspectroscopy are powerful complementary techniques which were employed to probe the biochemical composition of two AML cell lines in the presence and absence of BaP. Analysis was performed on single living cells along with dehydrated and fixed cells to provide a large and detailed data set. A consideration of the main spectral differences in conjunction with multivariate statistical analysis reveals a significant change to the cellular lipid composition with drug treatment; furthermore, this response is not caused by cell apoptosis. No change to the DNA of either cell line was observed suggesting this combination therapy primarily targets lipid biosynthesis or effects bioactive lipids that activate specific signalling pathways

Exploiting Cyclic Dependencies in Landmark Heuristics

Landmarks of a planning task denote properties that must be satisfied by all plans. Existing landmark heuristics exploit that each landmark must be achieved at least once. However, if the orderings between the landmarks induce cyclic dependencies, one of the landmarks in each cycle must be achieved an additional time. We propose two novel heuristics for cost-optimal planning that consider cyclic dependencies between landmarks in addition to the cost for achieving all landmarks once. We show that our heuristics dominate the minimum hitting set solution over any set of landmarks as well as h+ if all delete-relaxation landmarks are considered. An experimental evaluation on benchmarks from the International Planning Competition shows that exploiting cyclic dependencies can lead to improved heuristics

Zero-Knowledge Proofs for Classical Planning Problems

In classical planning, the aim is to find a sequence of deterministic actions leading from the initial to a goal state. In this work, we consider the scenario where a party who knows the solution to a planning task, called the prover, wants to convince a second party, the verifier, that it has the solution without revealing any information about the solution itself. This is relevant in domains where privacy is important, for example when plans contain sensitive information or when the solution should not be revealed upfront. We achieve this by introducing a zero-knowledge protocol for plan existence. By restricting ourselves to tasks with polynomially-bounded plan length, we are able to construct a protocol that can be run efficiently by both the prover and verifier. The resulting protocol does not rely on any reduction, has a constant number of rounds, and runs in time polynomial in the size of the task

Computing Domain Abstractions for Optimal Classical Planning with Counterexample-Guided Abstraction Refinement

Abstraction heuristics are the state of the art in optimal classical planning as heuristic search. A popular method for computing abstractions is the counterexample-guided abstraction refinement (CEGAR) principle, which has successfully been used for projections, which are the abstractions underlying pattern databases, and Cartesian abstractions. While projections are simple and fast to compute, Cartesian abstractions subsume projections and hence allow more finegrained abstractions, however at the expense of efficiency. Domain abstractions are a third class of abstractions between projections and Cartesian abstractions in terms of generality. Yet, to the best of our knowledge, they are only briefly considered in the planning literature but have not been used for computing heuristics yet. We aim to close this gap and compute domain abstractions by using the CEGAR principle. Our empirical results show that domain abstractions compare favorably against projections and Cartesian abstractions

Landmark Progression in Heuristic Search

The computation of high-quality landmarks and orderings for heuristic state-space search is often prohibitively expensive to be performed in every generated state. Computing information only for the initial state and progressing it from every state to its successors is a successful alternative, exploited for example in classical planning by the LAMA planner. We propose a general framework for using landmarks in any kind of best-first search. Its core component, the progression function, uses orderings and search history to determine which landmarks must still be achieved. We show that the progression function that is used in LAMA infers invalid information in the presence of reasonable orderings. We define a sound progression function that allows to exploit reasonable orderings in cost-optimal planning and show empirically that our new progression function is beneficial both in satisficing and optimal planning

Landmark Progression in Heuristic Search

The computation of high-quality landmarks and orderings for heuristic state-space search is often prohibitively expensive to be performed in every generated state. Computing information only for the initial state and progressing it from every state to its successors is a successful alternative, exploited for example in classical planning by the LAMA planner. We propose a general framework for using landmarks in any kind of best-first search. Its core component, the progression function, uses orderings and search history to determine which landmarks must still be achieved. We show that the progression function that is used in LAMA infers invalid information in the presence of reasonable orderings. We define a sound progression function that allows to exploit reasonable orderings in cost-optimal planning and show empirically that our new progression function is beneficial both in satisficing and optimal planning