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Saarland University


Compiling Probabilistic Model Checking into Probabilistic Planning

AAAI Conferences

It has previously been observed that the verification of safety properties in deterministic model-checking frameworks can be compiled into classical planning. A similar connection exists between goal probability analysis on either side, yet that connection has not been explored. We fill that gap with a translation from Jani, an input language for quantitative model checkers including the Modest toolset and PRISM, into PPDDL. Our experiments motivate further cross-fertilization between both research areas, specifically the exchange of algorithms. Our study also initiates the creation of new benchmarks for goal probability analysis.


On Stubborn Sets and Planning with Resources

AAAI Conferences

Stubborn sets are a well-established technique to admissibly prune permutable parts of forward search in classical planning. But what about planning with resources? The identification of effective stubborn sets relies on non-interfering actions. Yet, a priori, all actions affecting the same resource interfere. We show how to exploit the fact that, nevertheless, with commutativity of addition and subtraction, many resource-affecting action sequences are permutable. We design suitable notions of stubborn sets for planning with resources, with and without resource production. We show empirically, on classical IPC benchmarks with discrete resource variables, that our new pruning methods are often, and sometimes dramatically, superior to previous ones. Together with a novel way of automatically identifying the resource variables, this result holds under IPC conditions.


Simulated Penetration Testing as Contingent Planning

AAAI Conferences

In penetration testing (pentesting), network administrators attack their own network to identify and fix vulnerabilities. Planning-based simulated pentesting can achieve much higher testing coverage than manual pentesting. A key challenge is for the attack planning to imitate human hackers as faithfully as possible. POMDP models have been proposed to this end, yet they are computationally very hard, and it is unclear how to acquire the models in practice. At the other extreme, classical planning models are scalable and simple to obtain, yet completely ignore the incomplete knowledge characteristic of hacking. We propose contingent planning as a new middle ground, feasible in both computation burden and model acquisition effort while allowing for a representation of incomplete knowledge. We design the model, show how to adapt available solvers, and show how to acquire the model from real network scans in practice. We experiment on real networks and show that our approach scales to practical input sizes.


Improving Variational Encoder-Decoders in Dialogue Generation

AAAI Conferences

Variational encoder-decoders (VEDs) have shown promising results in dialogue generation. However, the latent variable distributions are usually approximated by a much simpler model than the powerful RNN structure used for encoding and decoding, yielding the KL-vanishing problem and inconsistent training objective. In this paper, we separate the training step into two phases: The first phase learns to autoencode discrete texts into continuous embeddings, from which the second phase learns to generalize latent representations by reconstructing the encoded embedding.  In this case, latent variables are sampled by transforming Gaussian noise through multi-layer perceptrons and are trained with a separate VED model, which has the potential of realizing a much more flexible distribution. We compare our model with current popular models and the experiment demonstrates substantial improvement in both metric-based and human evaluations.


Shen

AAAI Conferences

Variational encoder-decoders (VEDs) have shown promising results in dialogue generation. However, the latent variable distributions are usually approximated by a much simpler model than the powerful RNN structure used for encoding and decoding, yielding the KL-vanishing problem and inconsistent training objective. In this paper, we separate the training step into two phases: The first phase learns to autoencode discrete texts into continuous embeddings, from which the second phase learns to generalize latent representations by reconstructing the encoded embedding. In this case, latent variables are sampled by transforming Gaussian noise through multi-layer perceptrons and are trained with a separate VED model, which has the potential of realizing a much more flexible distribution. We compare our model with current popular models and the experiment demonstrates substantial improvement in both metric-based and human evaluations.


Complete Local Search: Boosting Hill-Climbing through Online Relaxation Refinement

AAAI Conferences

Several known heuristic functions can capture the input at different levels of precision, and support relaxation-refinement operations guaranteeing to converge to exact information in a finite number of steps. A natural idea is to use such refinement online, during search, yet this has barely been addressed. We do so here for local search, where relaxation refinement is particularly appealing: escape local minima not by search, but by removing them from the search surface. Thanks to convergence, such an escape is always possible. We design a family of hill-climbing algorithms along these lines. We show that these are complete, even when using helpful actions pruning. Using them with the partial delete relaxation heuristic hCFF, the best-performing variant outclasses FF's enforced hill-climbing, outperforms FF, outperforms dual-queue greedy best-first search with hFF, and in 6 IPC domains outperforms both LAMA and Mercury.


Critical-Path Dead-End Detection versus NoGoods: Offline Equivalence and Online Learning

AAAI Conferences

One traditional use of critical-path heuristic functions is as effective sufficient criteria for unsolvability. To employ this for dead-end detection, the heuristic function must be evaluated on every new state to be tested, incurring a substantial runtime overhead. We show herein that the exact same dead-end detector can be captured through a nogood, a formula phiOFF computed once prior to search. This is mostly of theoretical interest, as phiOFF is large. We obtain practical variants by instead incrementally generating a stronger nogood psi, that implies phiOFF, online during search, generalizing from already tested states to avoid future heuristic-function evaluations.


Fickert

AAAI Conferences

Several known heuristic functions can capture the input at different levels of precision, and support relaxation-refinement operations guaranteeing to converge to exact information in a finite number of steps. A natural idea is to use such refinement online, during search, yet this has barely been addressed. We do so here for local search, where relaxation refinement is particularly appealing: escape local minima not by search, but by removing them from the search surface. Thanks to convergence, such an escape is always possible.


Symmetry Breaking in Star-Topology Decoupled Search

AAAI Conferences

Symmetry breaking is a well-known method for search reduction. It identifies state-space symmetries prior to search, and prunes symmetric states during search. A recent proposal, star-topology decoupled search, is to search not in the state space, but in a factored version thereof, which avoids the multiplication of states across leaf components in an underlying star-topology structure. We show that, despite the much more complex structure of search states -- so-called decoupled states -- symmetry breaking can be brought to bear in this framework as well. Starting from the notion of structural symmetries over states, we identify a sub-class of such symmetries suitable for star-topology decoupled search, and we show how symmetries from that sub-class induce symmetry relations over decoupled states. We accordingly extend the routines required for search pruning and solution reconstruction. The resulting combined method can be exponentially better than both its components in theory, and this synergetic advantage is also manifested in practice: empirically, our method reliably inherits the best of its base components, and often outperforms them both.


Beyond Red-Black Planning: Limited-Memory State Variables

AAAI Conferences

Red-black planning delete-relaxes only some of the state variables. This is coarse-grained in that, for each variable, it either remembers all past values (red), or remembers only the most recent one (black). We herein introduce limited-memory state variables, that remember a subset of their most recent values. It turns out that planning is still PSPACE-complete even when the memory is large enough to store all but a single value. Nevertheless, limited memory can be used to substantially broaden a known tractable fragment of red-black planning, yielding better heuristic functions in some domains.