A decomposed symbolic approach to reactive planning

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2003.Includes bibliographical references (p. 105-108).by Seung H. Chung.S.M

Processing Succinct Matrices and Vectors

We study the complexity of algorithmic problems for matrices that are represented by multi-terminal decision diagrams (MTDD). These are a variant of ordered decision diagrams, where the terminal nodes are labeled with arbitrary elements of a semiring (instead of 0 and 1). A simple example shows that the product of two MTDD-represented matrices cannot be represented by an MTDD of polynomial size. To overcome this deficiency, we extended MTDDs to MTDD_+ by allowing componentwise symbolic addition of variables (of the same dimension) in rules. It is shown that accessing an entry, equality checking, matrix multiplication, and other basic matrix operations can be solved in polynomial time for MTDD_+-represented matrices. On the other hand, testing whether the determinant of a MTDD-represented matrix vanishes PSPACE$-complete, and the same problem is NP-complete for MTDD_+-represented diagonal matrices. Computing a specific entry in a product of MTDD-represented matrices is #P-complete.Comment: An extended abstract of this paper will appear in the Proceedings of CSR 201

Constructing Conditional Plans by a Theorem-Prover

The research on conditional planning rejects the assumptions that there is no uncertainty or incompleteness of knowledge with respect to the state and changes of the system the plans operate on. Without these assumptions the sequences of operations that achieve the goals depend on the initial state and the outcomes of nondeterministic changes in the system. This setting raises the questions of how to represent the plans and how to perform plan search. The answers are quite different from those in the simpler classical framework. In this paper, we approach conditional planning from a new viewpoint that is motivated by the use of satisfiability algorithms in classical planning. Translating conditional planning to formulae in the propositional logic is not feasible because of inherent computational limitations. Instead, we translate conditional planning to quantified Boolean formulae. We discuss three formalizations of conditional planning as quantified Boolean formulae, and present experimental results obtained with a theorem-prover

Multi-Domain Surety Modeling and Analysis for High Assurance Systems

Engineering systems are becoming increasingly complex as state of the art technologies am incorporated into designs. Surety modeling and analysis is an emerging science that permits an engineer to qualitatively and quantitatively predict and assess the completeness and predictability of a design. Surety is a term often used in the Department of Defense (DoD) and Department of Energy (DOE) communities, which refers to the integration of safety, security, reliability and performance aspects of design. Current risk assessment technologies for analyzing complex systems fail to adequately describe the problem, thus making assessment fragmented and non-integrated. To address this problem, we have developed a methodology and extensible software tool set to address model integration and complexity for high consequence systems. The MultiGraph Architecture (MGA) facilitates multi-domain, model-integrated modeling and analyses of complex, high-assurance systems. The MGA modeling environment allows the engineer to customize the modeling environment to match a design paradigm representative of the actual design. Previous modeling tools have a point-defined model space that forms the modeler to work in less than optimal environments. Current approaches for the problem to be bounded and constrained by requirements of the modeling tool and not the actual design problem. In some small cases, this is only maximally adequate MM facilitates the implementation of a surety methodology, which is used to represent high assurance systems with respect to safety and reliability. Formal mathematical models am used to correctly describe design safety and reliability functionality and behavioral fictional and behavioral representations of the design w then analyzed using commercial-off-the-shelf tools

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

09461 Abstracts Collection -- Algorithms and Applications for Next Generation SAT Solvers

From 8th to 13th November 2009, the Dagstuhl Seminar 09461 "Algorithms and Applications for Next Generation SAT Solvers" was held in Schloss Dagstuhl--Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts, slides or full papers are provided, if available