An overview of existing modeling tools making use of model checking in the analysis of biochemical networks

Model checking is a well-established technique for automaticallyverifying complex systems. Recently, model checkers have appearedin computer tools for the analysis of biochemical (and generegulatory) networks. We survey several such tools to assess thepotential of model checking in computational biology. Next, our overviewfocuses on direct applications of existing model checkers, as well ason algorithms for biochemical network analysis influenced by modelchecking, such as those using binary decision diagrams or Booleansatisfiability solvers. We conclude with advantages and drawbacks ofmodel checking for the analysis of biochemical networks

Fundamental Approaches to Software Engineering

computer software maintenance; computer software selection and evaluation; formal logic; formal methods; formal specification; programming languages; semantics; software engineering; specifications; verificatio

Design of an Autonomous Platform for Search and Rescue UAV Networks

This project designed and implemented a platform for use in a system of unmanned aerial vehicles (UAVs) capable of human assisted-autonomous and fully autonomous flight for search and rescue applications to improve the speed, efficiency, and safety of search and rescue to benefit both the victims and the rescuers alike. To accomplish this, the platform was designed to be lightweight with long endurance, equipped with specialized search and rescue sensors, and utilizes the paparazzi autopilot system, which is an open source, Linux based autopilot package for flight stability and autonomous control

Taming Numbers and Durations in the Model Checking Integrated Planning System

The Model Checking Integrated Planning System (MIPS) is a temporal least commitment heuristic search planner based on a flexible object-oriented workbench architecture. Its design clearly separates explicit and symbolic directed exploration algorithms from the set of on-line and off-line computed estimates and associated data structures. MIPS has shown distinguished performance in the last two international planning competitions. In the last event the description language was extended from pure propositional planning to include numerical state variables, action durations, and plan quality objective functions. Plans were no longer sequences of actions but time-stamped schedules. As a participant of the fully automated track of the competition, MIPS has proven to be a general system; in each track and every benchmark domain it efficiently computed plans of remarkable quality. This article introduces and analyzes the most important algorithmic novelties that were necessary to tackle the new layers of expressiveness in the benchmark problems and to achieve a high level of performance. The extensions include critical path analysis of sequentially generated plans to generate corresponding optimal parallel plans. The linear time algorithm to compute the parallel plan bypasses known NP hardness results for partial ordering by scheduling plans with respect to the set of actions and the imposed precedence relations. The efficiency of this algorithm also allows us to improve the exploration guidance: for each encountered planning state the corresponding approximate sequential plan is scheduled. One major strength of MIPS is its static analysis phase that grounds and simplifies parameterized predicates, functions and operators, that infers knowledge to minimize the state description length, and that detects domain object symmetries. The latter aspect is analyzed in detail. MIPS has been developed to serve as a complete and optimal state space planner, with admissible estimates, exploration engines and branching cuts. In the competition version, however, certain performance compromises had to be made, including floating point arithmetic, weighted heuristic search exploration according to an inadmissible estimate and parameterized optimization

Preparation and control of intelligent automation systems

In the automation systems of tomorrow, it is likely that the devices included have various degrees of autonomy, and include advanced algorithms for perception and control. Human operators will be expected to work together with collaborative robots as well as with roaming robots for material handling.The volatile nature of the environment of such intelligent automation systems lead to an enormous amount of possible situations that can arise and which need to be suitably handled. This complexity makes development of control systems for intelligent automation systems difficult using traditional methods.As an alternative, this thesis presents a model-based control framework, which uses a combination of formal specification and automated planning. The proposed framework allows for defining the intentions of the automation system on a high level, which enables decisions that influence when things should occur to be modeled using logical constraints, rather than programming. To achieve a modular framework, low level, reusable, resource models are composed by 1) formal specification to ensure safety and 2) applying an abstraction called an operation, which couples the reusable resources to the intentions of the system. By planning also the resources\u27 detailed actions, the operations can, when possible, be completed regardless of the resources\u27 current state. This eases error-recovery, as resources do not have to be reset when an error occurs.Additionally, the thesis proposes an iterative and interactive workflow for integrating the proposed model-based control framework into a virtual preparation process, using computer-based simulation as a tool for validating formal specifications. The control framework allows for adding new constraints to a running system, enabling an efficient and interactive preparation process.The framework has been applied to a use case from final assembly, which features human-robot collaboration. Experimental results on the ability to handle unforeseen errors and planning performance are presented

Solving parity games through fictitious play

The thesis aims to find an efficient algorithm for solving parity games. Parity games are graph-based, 0-sum, 2-person games with infinite plays. It is known that these games are determined: all nodes in these games are won by exactly one player. Solving parity games is equivalent to the model checking problem of modal mu-calculus; an efficient solution has important implications to program verification and controller synthesis. Although the decision problem of which player wins a given node is generally believed to be in PTIME, all known algorithms so far have been shown to run in (sub)exponential time. The design of existing algorithms either derives from the determinacy proof of parity games or from a purely graph theoretical perspective, using certain rank functions to iteratively search for an optimal solution. Since parity games are 2-person, 0-sum games, in this thesis I borrow ideas of game theory and investigate the viability of using fictitious play to solve them. Fictitious play is a method where two players choose strategies in strict alternation, and where these choices are “best responses” against the last k (so called bounded recall length) or against all strategies (unbounded recall length) of the other player chosen so far. I use this method to design an algorithm that can solve partity games and study its theoretical and experimental properties. For example, I prove that the basic algorithm solves fully connected games in polynomial time through a number of iterations that is bounded by a small constant. Although the proof is not extended to the general cases in the thesis, the basic algorithm performs demonstrably well against existing solvers in experiments over a large number and variety of games. In particular, the empirically obtained number of iterations that our basic algorithm requires appears to increase polynomially against the game sizes for all the games tested. Furthermore, the algorithm is conjectured to have a run time complexity bounded by O(n4 log2(n)) and I provide a discussion of strategy graphs and their emperically observed properties that motivates this conjecture. One caveat of fictitious play with bounded recall length is that the algorithm may fail to converge to the optimal solution due to the presence of nonoptimal strategy cycles of length greater than 2. In this thesis, I observe that in practice such cases account for less than 0.01% of the games tested. Different cycle resolution methods are explored in the thesis to address this. One particular method combines our basic algorithm and the discrete strategy solver together such that the resulting algorithm is guaranteed to terminate with the optimal solution. Also, this combined solver shares the runtime performance of fictitious play.Open Acces