Peter van Beek

Constraint propagation algorithms for temporal reasoning: a revised report

This paper revises and expands upon a paper presented by two of the present authors at AAAI 1986 [Vilain & Kautz 1986]. As with the original, this revised document considers computational aspects of interval-based and point-based temporal representations. Computing the consequences of temporal assertions is shown to be computationally intractable in the interval-based representation, but not in the point-based one. However, a fragment of the interval language can be expressed using the point language and benefits from the tractability of the latter. The present paper departs from the original primarily in correcting claims made there about the point algebra, and in presenting some closely related results of van Beek [1989].

Exact and approximate reasoning about temporal relations

Allen gives an algebra for representing qualitative temporal information about the relationships between pairs of intervals. In this paper, we address a fundamental reasoning task that arises in applications of the algebra: Given (possibly indefinite) knowledge about the relationships between intervals, find all feasible relationships between two intervals. We call this the minimal labels problem. Finding the minimal labels can be viewed as computing the deductive consequences of our knowledge. Determining exact solutions to this problem has been shown to be (almost assuredly) intractable. Allen gives an approximation algorithm based on constraint propagation. We present new approximation algorithms; determine analytically under what conditions the algorithms are exact; and examine, through some computational experiments, the quality of the approximate solutions produced by the algorithms. We also give a simple test for predicting when the approximation algorithms will and will not produce good quality approximations. Finally, we survey three example applications of the interval algebra chosen from the literature to show where the results of this paper could be useful.

Reasoning about qualitative temporal information

Interval and point algebras have been proposed for representing qualitative temporal information about the relationships between pairs of intervals and pairs of points, respectively. In this paper, we address two related reasoning tasks that arise in these algebras: Given (possibly indefinite) knowledge of the relationships between some intervals or points, (1) find one or more scenarios that are consistent with the information provided, and (2) find all the feasible relations between every pair of intervals or points. Solutions to these problems have applications in natural language processing, planning, and a knowledge representation language. We define computationally efficient procedures for solving these tasks for the point algebra and for a corresponding subset of the interval algebra. Our algorithms are marked improvements over the previously known algorithms. We also show how the results for the point algebra aid in the design of a backtracking algorithm for the full interval algebra that is useful in practice.

On the minimality and global consistency of row-convex constraint networks

Constraint networks have been shown to be useful in formulating such diverse problems as scene labeling, natural language parsing, and temporal reasoning. Given a constraint network, we often wish to (i) find a solution that satisfies the constraints and (ii) find the corresponding minimal network where the constraints are as explicit as possible. Both tasks are known to be NP-complete in the general case. Task (1) is usually solved using a backtracking algorithm, and task (ii) is often solved only approximately by enforcing various levels of local consistency. In this paper, we identify a property of binary constraint called row convexity and show its usefulness in deciding when a form of local consistency called path consistency is sufficient to guarantee that a network is both minimal and globally consistent. Globally consistent networks have the property that a solution can be found without backtracking. We show that one can test for the row convexity property efficiently and we show, by examining applications of constraint networks discussed in the literature, that our results are useful in practice. Thus, we identify a class of binary constraint networks for which we can solve both tasks (i) and (ii) efficiently. Finally, we generalize the results for binary constraint networks to networks with nonbinary constraints.

Local and global relational consistency

Local consistency has proven to be an important concept in the theory and practice of constraint networks. In this paper, we present a new definition of local consistency, called relational consistency. The new definition is relationbased, in contrast with the previous definition of local consistency, which we characterize as variable-based. It allows the unification of known elimination operators such as resolution in theorem proving, joins in relational databases and variable elimination for solving linear inequalities. We show the usefulness and conceptual power of the new definition in characterizing relationships between four properties of constraints — domain tightness, row-convexity, constraint tightness, and constraint looseness — and the level of local consistency needed to ensure global consistency. As well, algorithms tor enforcing relational consistency are introduced and analyzed.

The design and experimental analysis of algorithms for temporal reasoning

Many applications-from planning and scheduling to problems in molecular biology-rely heavily on a temporal reasoning component. In this paper, we discuss the design and empirical analysis of algorithms for a temporal reasoning system based on Allens influential interval-based framework for representing temporal information. At the core of the system are algorithms for determining whether the temporal information is consistent, and, if so, finding one or more scenarios that are consistent with the temporal information. Two important algorithms for these tasks are a path consistency algorithm and a backtracking algorithm. For the path consistency algorithm, we develop techniques that can result in up to a ten-fold speedup over an already highly optimized implementation. For the backtracking algorithm, we develop variable and value ordering heuristics that are shown empirically to dramatically improve the performance of the algorithm. As well, we show that a previously suggested reformulation of the backtracking search problem can reduce the time and space requirements of the backtracking search. Taken together, the techniques we develop allow a temporal reasoning component to solve problems that are of practical size.

A Model For Generating Better Explanations

Previous work in generating explanations from advice-giving systems has demonstrated that a cooperative system can and should infer the immediate goals and plans of an utterance (or discourse segment) and formulate a response in light of these goals and plans. The claim of this paper is that a cooperative response may also have to address a users overall goals, plans, and preferences among those goals and plans. An algorithm is introduced that generates user-specific responses by reasoning about the goals, plans and preferences hypothesized about a user.

Backtracking Search Algorithms

Publisher Summary This chapter explores that there are three main algorithmic techniques for solving constraint satisfaction problems: (1) backtracking search, (2) local search, and (3) dynamic programming. An algorithm for solving a constraint satisfaction problem (CSP) can be either complete or incomplete. Complete or systematic algorithms, come with a guarantee of a solution if one exists, and can be used to show that a CSP does not have a solution and to find a provably optimal solution. Backtracking search algorithms and dynamic programming algorithms are examples of complete algorithms. Incomplete, or nonsystematic algorithms, cannot be used to show a CSP does not have a solution or to find a provably optimal solution. However, such algorithms are often effective at finding a solution if one exists and can be used to find an approximation to an optimal solution. Of the two classes of algorithms that are complete—backtracking search and dynamic programming—backtracking search algorithms are currently the most important in practice. These work on only one solution at a time and thus need only a polynomial amount of space.

Temporal query processing with indefinite information

Time is an important aspect of information in medical domains. In this paper, we adopt Allens influential interval algebra framework for representing temporal information. The interval algebra allows the representation of indefinite and incomplete information which is necessary in many applications. However, answering interesting queries in this framework has been shown to be almost assuredly intractable. We show that when the representation language is sufficiently restricted we can develop efficient algorithms for answering interesting classes of queries including: (i) determining whether a formula involving temporal relations between events is possibly true and necessarily true; and (ii) answering aggregation questions where the set of all events that satisfy a formula are retrieved. We also show, by examining applications of the interval algebra discussed in the literature, that our restriction on the representation language often is not overly restrictive in practice.

Binary vs. non-binary constraints

There are two well known transformations from non-binary constraints to binary constraints applicable to constraint satisfaction problems (CSPs) with finite domains: the dual transformation and the hidden (variable) transformation. We perform a detailed formal comparison of these two transformations. Our comparison focuses on two backtracking algorithms that maintain a local consistency property at each node in their search tree: the forward checking and maintaining arc consistency algorithms. We first compare local consistency techniques such as arc consistency in terms of their inferential power when they are applied to the original (non-binary) formulation and to each of its binary transformations. For example, we prove that enforcing arc consistency on the original formulation is equivalent to enforcing it on the hidden transformation. We then extend these results to the two backtracking algorithms. We are able to give either a theoretical bound on how much one formulation is better than another, or examples that show such a bound does not exist. For example, we prove that the performance of the forward checking algorithm applied to the hidden transformation of a problem is within a polynomial bound of the performance of the same algorithm applied to the dual transformation of the problem. Our results can be used to help decide if applying one of these transformations to all (or part) of a constraint satisfaction model would be beneficial.There are two well known transformations from non-binary constraints to binary constraints applicable to constraint satisfaction problems (CSPs) with finite domains: the dual transformation and the...