Lina Khatib

DOMAIN-INDEPENDENT TEMPORAL REASONING WITH RECURRING EVENTS

Numerous examples of temporal reasoning involve a process of abstraction from the number of times an event is to occur or the number of times events stand in a temporal relation. For example, scheduling a recurring event such as ones office hours may consider things like the relative temporal ordering of the office hours and a number of other events in a given work day. The number of times office hours will actually be held may be unknown, even irrelevant, at the time of scheduling them. The objective of this article is to formulate a domain‐independent framework for reasoning about recurring events and their relations. To achieve this end, we propose an ontology of recurrence based on the model‐theoretic structure underlying collective predication using plural noun phrases. We offer a calculus of binary temporal relations for temporal collections based on a well‐defined transformation of interval temporal relations into recurrence relations. Finally, we describe a reasoning framework based on manipulating knowledge stored in temporal relation networks, which is in turn a specialization of the CSP (constraint satisfaction problem) framework. The reasoner manipulates recurrence relations in the network to determine the networks consistency or to generate scenarios.

Mapping temporal planning constraints into timed automata

Planning and model checking are similar in concept. They both deal with reaching a goal state from an initial state by applying specified rules that allow for the transition from one state to another. Exploring the relationship between them is an interesting new research area. We are interested in planning frameworks that combine both planning and scheduling. For that, we focus our attention on real time model checking. As a first step, we developed a mapping from planning domain models into timed automata. Since timed automata are the representation structure of real-time model checkers, we are able to exploit what model checking has to offer for planning domains. We present the mapping algorithm, which involves translating temporal specifications into timed automata, and list some of the planning domain questions someone can answer by using model checking.

Learning and Solving Soft Temporal Constraints: An Experimental Study

Soft temporal constraints problems allow for a natural description of scenarios where events happen over time and preferences are associated with event distances and durations. However, sometimes such local preferences are difficult to set, and it may be easier instead to associate preferences to some complete solutions of the problem, and then to learn from them suitable preferences over distances and durations. In this paper, we describe our learning algorithm and we show its behaviour on classes of randomly generated problems. Moreover, we also describe two solvers (one more general and the other one more efficient) for tractable subclasses of soft temporal problems, and we give experimental results to compare them.

Quantitative structural temporal constraints on repeating events

Presents a model of the temporal structure of repeating events. This model allows for the specification of constraints dealing with the part-whole structure of these events. The temporal structure of repeating events is viewed as being composed of six aspects: sub-interval number, gap number, sub-interval duration, gap duration, period and extent. A set of constraints involving these aspects collectively specifies the conditions under which repeating events can be assigned times, and thus partially formalizes a solution to the scheduling problem for repeating events. The consistency and tightness of a set of such constraints can be tested by identifying determinacy relationships among the different aspects. These relationships can also be used to infer constraints about one structural aspect from others. This paper also introduces a new category of quantitative temporal constraints involving repeating events which specifies that the duration or period of events be distributed randomly over a set of values.

Local Search for Optimal Global Map Generation Using Mid-Decadal Landsat Images

NASA and the US Geological Survey (USGS) are generating image maps of the entire Earth using Landsat 5 Thematic Mapper (TM) and Landsat 7 (L7) Enhanced Thematic Mapper Plus (ETM+) sensor data from the period of 2004 through 2007. The map is comprised of thousands of scene locations and, for each location, there are tens of different images of varying quality to chose from. Constraints and preferences on map quality make it desirable to develop an automated solution to the map generation problem. This paper formulates a Global Map Generator problem as a Constraint Optimization Problem (GMG-COP) and describes an approach to solving It usmg local search. The paper also describes the integration of a GMG solver into a user interface for visualizing and comparing solutions.

Reasoning with sequences of point events

Proposes the modeling of recurring events as multi-point events by extending Vilain and Kautzs (1986) point algebra. We then propose an exact algorithm, based on van Beeks (1990) exact algorithm, for finding feasible relations for multi-point event networks. The complexity of our method is compared with previously known results both for recurring and non-recurring events. We identify the special cases for which our multi-point based algorithm can find an exact solution. Finally, we summarise our paper with brief discussion on ongoing and future research.

Learning preferences on temporal constraints: a preliminary report

A number of reasoning problems involving the manipulation of temporal information can naturally be viewed as implicitly inducing an ordering of potential local decisions involving time (specifically, associated with durations or orderings of events) on the basis of preferences. For example, a pair of events might be constrained to occur in a certain order and, in addition, it might be preferable that the delay between the start times of each of them be as large, or as small, as possible. Sometimes, however, it is more natural to view preferences as something initially ascribed to complete solutions to temporal reasoning problems, rather than to local decisions. For example, in classical scheduling problems, the preference for solutions which minimize makespan is a global, rather than a local, condition. In such cases, it might be useful to learn the local preferences that contribute to globally preferred solutions. This information could be used in heuristics to guide the solver to more promising solutions. To address the potential requirement for information about local preferences, we propose to apply learning techniques to infer local preferences from global ones. The preliminary work proposes an approach based on the notion of learning a set of soft temporal constraints, given a training set of solutions to a Temporal CSP, and an objective function for evaluating each solution in the set.

Representation and Reasoning with Multi-Point Events

Allens Interval Algebra (IA) and Vilain & Kautzs Point Algebra (PA) consider an interval and a point as basic temporal entities (i.e., events) respectively. However, in many situations we need to deal with recurring events that include multiple points, multiple intervals or combinations of points and intervals. In this paper, we present a framework to model recurring events as multi-point events (MPEs) by extending point algebra. The reasoning tasks are formulated as binary constraint satisfaction problems. We propose a polynomial time algorithm (based on van Beeks algorithm) for finding all feasible relations. For the problem of finding a consistent scenario, we propose a backtracking method with a local search heuristic. We also describe an implementation and a detail empirical evaluation of the proposed algorithms. Our empirical results indicate that the MPE-based approach performs better than the existing approaches.

A Generalized Framework for Reasoning with Multi-Point Events

Allens Interval Algebra (IA) and Vilain and Kautzs Point Algebra (PA) consider an interval and a point as basic temporal entities (i.e., events) respectively. However, in many real world situations we often need to deal with recurring events that include multiple points, multiple intervals or combinations of points and intervals. Recently, we presented a multiple-point event (MPE) framework to represent relations over recurring point events and showed that it can handle pointisable interval relations (SIA). We also showed that computing a minimal MPE network is a polynomial solvable problem. However, the MPE framework cannot correctly capture the relation between three points called a discontinuous point relation and this has not been satisfactorily addressed in the literature. In this paper, we extend MPE to a general framework that is expressive enough to represent discontinuous point relations and other complex situations which are relationships between single events (i.e., point-interval, and interval-interval relations), and clusters of events (i.e., recurring point-point and interval-interval relations). Further we developed a path-consistency algorithm for computing the minimal network for a generalised MPE network and improved our earlier path-consistency algorithm for MPE networks. We then present an analysis of experimental results on the implementation of these algorithms.

Reasoning with Multi-Point Events

Recent research on qualitative reasoning has focussed on representing and reasoning about events that occur repeatedly. Allens interval algebra has been modified to model events that are collections of convex intervals—a non-convex interval. Using the modified version of Allens algebra, constraint-based algorithms have been investigated for finding feasible relations in a network of non-convex intervals.