Force deployment analysis with generalized grammar

Abstract

We analyse the key algorithms of data and information fusion from a linguistic point of view, and show that they fall into two paradigms: the primarily syntactic, and the primarily semantic. We propose an alternative grammatical paradigm which exploits the ability of grammar to combine syntactic inference with semantic representation. We generalize the concept of formal generative grammar to include multiple rule classes each having a topology and a base vocabulary. A generalized Chomsky hierarchy is defined. Analysing fusion algorithms in terms of grammatical representations, we find that most (including multiple hypothesis tracking) can be expressed in terms of conventional regular grammars. Situation analysis, however, is commonly attempted using first order predicate logic, which while expressive, is recursively enumerable and so scales badly. We argue that the core issue in situation assessment is force deployment assessment, the extraction and scoring of hypotheses of the force deployment history, each of which is a multiresolution account of the activities, groupings and interactions of force components. The force deployment history represents these relationships at multiple levels of granularity and is expressed over time and space. We provide a grammatical approach for inferring such histories, and show that they can be estimated accurately and scalably. We employ a generalized context-free grammar incorporating both sequence and multiset productions. Elaborating [D. McMichael, G. Jarrad, S. Williams, M. Kennett, Grammatical methods for situation and threat analysis, in: Proceedings of The 8th International Conference on Information Fusion, Philadelphia, PA, 2005], a Generalized Functional Combinatory Categorial Grammar (GFCCG) is described that is both generalized and semantically functional (in that the semantics can be calculated directly from the syntax using a small number of rules). Force deployment modelling and parsing is demonstrated in naval and air defence scenarios. Simulation studies indicate that the method robustly handles the errors introduced by trackers under noisy cluttered conditions. The empirical time complexity of batch force deployment parsing is better than O ( N 1.5 ) , where N is the number of track segments. Force deployment assessments are required in real-time, and we have developed an incremental parser that keeps up with real-time data, and fulfils at Level 2 in the JDL fusion hierarchy the role that trackers fulfil at Level 1.