Robert Mattmüller

Using the context-enhanced additive heuristic for temporal and numeric planning

Planning systems for real-world applications need the ability to handle concurrency and numeric fluents. Nevertheless, the predominant approach to cope with concurrency followed by the most successful participants in the latest International Planning Competitions (IPC) is still to find a sequential plan that is rescheduled in a post-processing step. We present Temporal Fast Downward (TFD), a planning system for temporal problems that is capable of finding low-makespan plans by performing a heuristic search in a temporal search space. We show how the context-enhanced additive heuristic can be successfully used for temporal planning and how it can be extended to numeric fluents. TFD often produces plans of high quality and, evaluated according to the rating scheme of the last IPC, outperforms all state-of-the-art temporal planning systems.

Synthia: verification and synthesis for timed automata

We present Synthia, a new tool for the verification and synthesis of open real-time systems modeled as timed automata. The key novelty of Synthia is the underlying abstraction refinement approach [5] that combines the efficient symbolic treatment of timing information by difference bound matrices (DBMs) with the usage of binary decision diagrams (BDDs) for the discrete parts of the system description. Our experiments show that the analysis of both closed and open systems greatly benefits from identifying large relevant and irrelevant system parts on coarse abstractions early in the solution process. Synthia is licensed under the GNU GPL and available from our website.

Pattern database heuristics for fully observable nondeterministic planning

When planning in an uncertain environment, one is often interested in finding a contingent plan that prescribes appropriate actions for all possible states that may be encountered during the execution of the plan. We consider the problem of finding strong cyclic plans for fully observable nondeterministic (FOND) planning problems. The algorithm we choose is LAO*, an informed explicit state search algorithm. We investigate the use of pattern database (PDB) heuristics to guide LAO* towards goal states. To obtain a fully domain-independent planning system, we use an automatic pattern selection procedure that performs local search in the space of pattern collections. The evaluation of our system on the FOND benchmarks of the Uncertainty Part of the International Planning Competition 2008 shows that our approach is competitive with symbolic regression search in terms of problem coverage, speed, and plan quality.

A stubborn set algorithm for optimal planning

We adapt a partial order reduction technique based on stubborn sets, originally proposed for detecting dead ends in Petri Nets, to the setting of optimal planning. We demonstrate that stubborn sets can provide significant state space reductions on standard planning benchmarks, outperforming the expansion core method.

Combining symbolic representations for solving timed games

We present a general approach to combine symbolic state space representations for the discrete and continuous parts in the synthesis of winning strategies for timed reachability games. The combination is based on abstraction refinement where discrete symbolic techniques are used to produce a sequence of abstract timed game automata. After each refinement step, the resulting abstraction is used for computing an under- and an over-approximation of the timed winning states. The key idea is to identify large relevant and irrelevant parts of the precise weakest winning strategy already on coarse, and therefore simple, abstractions. If neither the existence nor nonexistence of a winning strategy can be established in the approximations, we use them to guide the refinement process. Based on a prototype that combines binary decision diagrams [7,9] and difference bound matrices [5], we experimentally evaluate the technique on standard benchmarks from timed controller synthesis. The results clearly demonstrate the potential of the new approach concerning running time and memory consumption compared to the classical on-the-fly algorithm implemented in UPPAAL-TIGA [10,4].

Component-Based Abstraction Refinement for Timed Controller Synthesis

We present a novel technique for synthesizing controllers for distributed real-time environments with safety requirements. Our approach is an abstraction refinement extension to the on-the-fly algorithm by Cassez et al. from 2005. Based on partial compositions of some environment components, each refinement cycle constructs a sound abstraction that can be used to obtain under- and over-approximations of all valid controller implementations. This enables (1) early termination if an implementation does not exist in the over-approximation, or, if one does exist in the under-approximation, and (2) pruning of irrelevant moves in subsequent refinement cycles. In our refinement loop, the precision of the abstractions incrementally increases and converges to all specification-critical components. We implemented our approach in a prototype synthesis tool and evaluated it on an industrial benchmark. In comparison with the timed game solver UPPAAL-Tiga, our technique outperforms the nonincremental approach by an order of magnitude.

An Experimental Comparison of Classical, FOND and Probabilistic Planning

Domain-independent planning in general is broadly applicable to a wide range of tasks. Many formalisms exist that allow the description of different aspects of realistic problems. Which one to use is often no obvious choice, since a higher degree of expressiveness usually comes with an increased planning time and/or a decreased policy quality. Under the assumption that hard guarantees are not required, users are faced with a decision between multiple approaches. As a generic model we use a probabilistic description in the form of Markov Decision Processes (MDPs). We define abstracting translations into a classical planning formalism and fully observable nondeterministic planning. Our goal is to give insight into how state-of-the-art systems perform on different MDP planning domains.

Cooperative epistemic multi-agent planning for implicit coordination

Epistemic planning can be used for decision making in multi-agent situations with distributed knowledge and capabilities. Recently, Dynamic Epistemic Logic (DEL) has been shown to provide a very natural and expressive framework for epistemic planning. We extend the DEL-based epistemic planning framework to include perspective shifts, allowing us to define new notions of sequential and conditional planning with implicit coordination. With these, it is possible to solve planning tasks with joint goals in a decentralized manner without the agents having to negotiate about and commit to a joint policy at plan time. First we define the central planning notions and sketch the implementation of a planning system built on those notions. Afterwards we provide some case studies in order to evaluate the planner empirically and to show that the concept is useful for multi-agent systems in practice.

Solving non-deterministic planning problems with pattern database heuristics

Non-determinism arises naturally in many real-world applications of action planning. Strong plans for this type of problems can be found using AO* search guided by an appropriate heuristic function. Most domain-independent heuristics considered in this context so far are based on the idea of ignoring delete lists and do not properly take the non-determinism into account. Therefore, we investigate the applicability of pattern database (PDB) heuristics to nondeterministic planning. PDB heuristics have emerged as rather informative in a deterministic context. Our empirical results suggest that PDB heuristics can also perform reasonably well in non-deterministic planning. Additionally, we present a generalization of the pattern additivity criterion known from classical planning to the non-deterministic setting.

Selective approaches for solving weak games

Model-checking alternating-time properties has recently attracted much interest in the verification of distributed protocols. While checking the validity of a specification in alternating-time temporal logic (ATL) against an explicit model is cheap (linear in the size of the formula and the model), the problem becomes EXPTIME-hard when symbolic models are considered. Practical ATL model-checking therefore often consumes too much computation time to be tractable. In this paper, we describe a novel approach for ATL model-checking, which constructs an explicit weak model-checking game on-the-fly. This game is then evaluated using heuristic techniques inspired by efficient evaluation algorithms for and/or-trees. To show the feasibility of our approach, we compare its performance to the ATL model-checking system MOCHA on some practical examples. Using very limited heuristic guidance, we achieve a significant speedup on these benchmarks.