2 research outputs found
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A complete reified temporal logic and its applications
Temporal representation and reasoning plays a fundamental and increasingly important role in some areas of Computer Science and Artificial Intelligence. A natural approach to represent and reason about time-dependent knowledge is to associate them with instantaneous time points and/or durative time intervals. In particular, there are various ways to use logic formalisms for temporal knowledge representation and reasoning. Based on the chosen logic frameworks, temporal theories can be classified into modal logic approaches (including prepositional modal logic approaches and hybrid logic approaches) and predicate logic approaches (including temporal argument methods and temporal reification methods). Generally speaking, the predicate logic approaches are more expressive than the modal logic approaches and among predicate logic approaches, temporal reification methods are even more expressive for representing and reasoning about general temporal knowledge. However, the current reified temporal logics are so complicate that each of them either do not have a clear definition of its syntax and semantics or do not have a sound and complete axiomatization.
In this thesis, a new complete reified temporal logic (CRTL) is introduced which has a clear syntax, semantics, and a complete axiomatic system by inheriting from the initial first order language. This is the main improvement made to the reification approaches for temporal representation and reasoning. It is a true reified logic since some meta-predicates are formally defined that allow one to predicate and quantify over prepositional terms, and therefore provides the expressive power to represent and reason about both temporal and non-temporal relationships between prepositional terms.
For a special case, the temporal model of the simplified CRTL system (SCRTL) is defined as scenarios and graphically represented in terms of a directed, partially weighted or attributed, simple graph. Therefore, the problem of matching temporal scenarios is transformed into conventional graph matching.
For the scenario graph matching problem, the traditional eigen-decomposition graph matching algorithm and the symmetric polynomial transform graph matching algorithm are critically examined and improved as two new algorithms named meta-basis graph matching algorithm and sort based graph matching algorithm respectively, where the meta-basis graph matching algorithm works better for 0-1 matrices while the sort based graph matching algorithm is more suitable for continuous real matrices.
Another important contribution is the node similarity graph matching framework proposed in this thesis, based on which the node similarity graph matching algorithms can be defined, analyzed and extended uniformly. We prove that that all these node similarity graph matching algorithms fail to work for matching circles
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A general state-based temporal pattern recognition
Time-series and state-sequences are ubiquitous patterns in temporal logic and are widely used to present temporal data in data mining. Generally speaking, there are three known choices for the time primitive: points, intervals, points and intervals. In this thesis, a formal characterization of time-series and state-sequences is presented for both complete and incomplete situations, where a state-sequence is defined as a list of sequential data validated on the corresponding time-series. In addition, subsequence matching is addressed to associate the state-sequences, where both non-temporal aspects as well as rich temporal aspects including temporal order, temporal duration and temporal gap should be taken into account.
Firstly, based on the typed point based time-elements and time-series, a formal characterization of time-series and state-sequences is introduced for both complete and incomplete situations, where a state-sequence is defined as a list of sequential data validated on the corresponding time-series. A time-series is formalized as a tetrad (T, R, Tdur, Tgap), which denotes: the temporal order of time- elements; the temporal relationship between time-elements; the temporal duration of each time-element and the temporal gap between each adjacent pair of time-elements respectively.
Secondly, benefiting from the formal characterization of time-series and state-sequences, a general similarity measurement (GSM) that takes into account both non-temporal and rich temporal information, including temporal order as well as temporal duration and temporal gap, is introduced for subsequence matching. This measurement is general enough to subsume most of the popular existing measurements as special cases. In particular, a new conception of temporal common subsequence is proposed. Furthermore, a new LCS-based algorithm named Optimal Temporal Common Subsequence (OTCS), which takes into account rich temporal information, is designed. The experimental results on 6 benchmark datasets demonstrate the effectiveness and robustness of GSM and its new case OTCS. Compared with binary-value distance measurements, GSM can distinguish between the distance caused by different states in the same operation; compared with the real-penalty distance measurements, it can filter out the noise that may push the similarity into abnormal levels.
Finally, two case studies are investigated for temporal pattern recognition: basketball zone-defence detection and video copy detection.
In the case of basketball zone-defence detection, the computational technique and algorithm for detecting zone-defence patterns from basketball videos is introduced, where the Laplacian Matrix-based algorithm is extended to take into account the effects from zoom and single defender‘s translation in zone-defence graph matching and a set of character-angle based features was proposed to describe the zone-defence graph. The experimental results show that the approach explored is useful in helping the coach of the defensive side check whether the players are keeping to the correct zone-defence strategy, as well as detecting the strategy of the opponent side. It can describe the structure relationship between defender-lines for basketball zone-defence, and has a robust performance in both simulation and real-life applications, especially when disturbances exist.
In the case of video copy detection, a framework for subsequence matching is introduced. A hybrid similarity framework addressing both non-temporal and temporal relationships between state-sequences, represented by bipartite graphs, is proposed. The experimental results using real-life video databases demonstrated that the proposed similarity framework is robust to states alignment with different numbers and different values, and various reordering including inversion and crossover