Computing and Information Systems - Research Publications
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ItemNo Preview AvailableSequencing operator counts(AAAI Press, 2016-01-01)Copyright 2016 AAAI, all rights reserved.). Operator-counting is a recently developed framework for analysing and integrating many state-ofthe- art heuristics for planning using Linear Programming. In cost-optimal planning only the objective value of these heuristics is traditionally used to guide the search. However the primal solution, i.e. the operator counts, contains useful information. We exploit this information using a SATbased approach which given an operator-count, either finds a valid plan; or generates a generalized landmark constraint violated by that count. We show that these generalized landmarks can be used to encode the perfect heuristic, h∗, as a Mixed Integer Program. Our most interesting experimental result is that finding or refuting a sequence for an operator-count is most often empirically efficient, enabling a novel and promising approach to planning based on Logic-Based Benders Decomposition (LBBD). This paper originally appeared at ICAPS 2015 and is reproduced with the permission of the Association for Artificial Intelligence ([Davies et al., 2015]
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ItemAbstract Interpretation over Non-Lattice Abstract Domains(Springer, 2013)The classical theoretical framework for static analysis of programs is abstract interpretation. Much of the power and elegance of that framework rests on the assumption that an abstract domain is a lattice. Nonetheless, and for good reason, the literature on program analysis provides many examples of non-lattice domains, including non-convex numeric domains. The lack of domain structure, however, has negative consequences, both for the precision of program analysis and for the termination of standard Kleene iteration. In this paper we explore these consequences and present general remedies.
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ItemNo Preview AvailableDynamic Programming for Predict plus Optimise(ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE, 2020)We study the predict+optimise problem, where machine learning and combinatorial optimisation must interact to achieve a common goal. These problems are important when optimisation needs to be performed on input parameters that are not fully observed but must instead be estimated using machine learning. We provide a novel learning technique for predict+optimise to directly reason about the underlying combinatorial optimisation problem, offering a meaningful integration of machine learning and optimisation. This is done by representing the combinatorial problem as a piecewise linear function parameterised by the coefficients of the learning model and then iteratively performing coordinate descent on the learning coefficients. Our approach is applicable to linear learning functions and any optimisation problem solvable by dynamic programming. We illustrate the effectiveness of our approach on benchmarks from the literature.
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ItemDashed Strings and the Replace(-all) Constraint(Springer International Publishing, 2020)Dashed strings are a formalism for modelling the domain of string variables when solving combinatorial problems with string constraints. In this work we focus on (variants of) the Replace constraint, which aims to find the first occurrence of a query string in a target string, and (possibly) replaces it with a new string. We define a Replace propagator which can also handle Replace-Last (for replacing the last occurrence) and Replace-All (for replacing all the occurrences). Empirical results clearly show that string constraint solving can draw great benefit from this approach.
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ItemOptimal Bounds for Floating-Point Addition in Constant Time(IEEE, 2019-06)Reasoning about floating-point numbers is notoriously difficult, owing to the lack of convenient algebraic properties such as associativity. This poses a substantial challenge for program analysis and verification tools which rely on precise floating-point constraint solving. Currently, interval methods in this domain often exhibit slow convergence even on simple examples. We present a new theorem supporting efficient computation of exact bounds of the intersection of a rectangle with the preimage of an interval under floating-point addition, in any radix or rounding mode. We thus give an efficient method of deducing optimal bounds on the components of an addition, solving the convergence problem.
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ItemA novel approach to string constraint solving(Springer, 2017-01-01)String processing is ubiquitous across computer science, and arguably more so in web programming. In order to reason about programs manipulating strings we need to solve constraints over strings. In Constraint Programming, the only approaches we are aware for representing string variables—having bounded yet possibly unknown size—degrade when the maximum possible string length becomes too large. In this paper, we introduce a novel approach that decouples the size of the string representation from its maximum length. The domain of a string variable is dynamically represented by a simplified regular expression that we called a dashed string, and the constraint solving relies on propagation of information based on equations between dashed strings. We implemented this approach in G-Strings, a new string solver—built on top of Gecode solver—that already shows some promising results.
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ItemSweep-based propagation for string constraint solving(Association for the Advancement of Artificial Intelligence, 2018-01-01)Solving constraints over strings is an emerging important field. Recently, a Constraint Programming approach based on dashed strings has been proposed to enable a compact domain representation for potentially large bounded-length string variables. In this paper, we present a more efficient algorithm for propagating equality (and related constraints) over dashed strings. We call this propagation sweep-based. Experimental evidences show that sweep-based propagation is able to significantly outperform state-of-the-art approaches for string constraint solving.
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ItemPropagating lex, find and replace with dashed strings(Springer International Publishing, 2018-01-01)Dashed strings have been recently proposed in Constraint Programming to represent the domain of string variables when solving combinatorial problems over strings. This approach showed promising performance on some classes of string problems, involving constraints like string equality and concatenation. However, there are a number of string constraints for which no propagator has yet been defined. In this paper, we show how to propagate lexicographic ordering (lex), find and replace with dashed strings. All of these are fundamental string operations: lex is the natural total order over strings, while find and replace are frequently used in string manipulation. We show that these propagators, that we implemented in G-Strings solver, allows us to be competitive with state-of-the-art approaches.
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ItemPropagating Regular membership with dashed strings(Springer Nature, 2018-01-01)Using dashed strings is an approach recently introduced in Constraint Programming (CP) to represent the domain of string variables, when solving combinatorial problems with string constraints. One of the most important string constraints is that of regular membership: regular (x, R) imposes string x to be a member of the regular language defined by automaton R. The regular constraint is useful for specifying complex constraints on fixed length finite sequences, and regularly appears in CP models. Dealing with regular is also desirable in software testing and verification, because regular expressions are often used in modern programming languages for pattern matching. In this paper, we define a regular propagator for dashed string solvers. We show that this propagator, implemented in the G-Strings solver, is substantially better than the current state-of-the-art. We also demonstrate that many regular constraints appearing in string solving benchmarks can actually be tackled by dashed strings solvers without explicitly using REGULAR.
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ItemMiniZinc with Strings(SPRINGER INTERNATIONAL PUBLISHING AG, 2017-01-01)Strings are extensively used in modern programming languages and constraints over strings of unknown length occur in a wide range of real-world applications such as software analysis and verification, testing, model checking, and web security. Nevertheless, practically no constraint programming solver natively supports string constraints. We introduce string variables and a suitable set of string constraints as builtin features of the MiniZinc modelling language. Furthermore, we define an interpreter for converting a MiniZinc model with strings into a FlatZinc instance relying only on integer variables. This conversion is obtained via rewrite rules, and does not require any extension of the existing FlatZinc specification. This provides a user-friendly interface for modelling combinatorial problems with strings, and enables both string and non-string solvers to actually solve such problems.