Spatio-Temporal Reasoning About Agent Behavior

There are many applications where we wish to reason about spatio-temporal aspects of an agent's behavior. This dissertation examines several facets of this type of reasoning. First, given a model of past agent behavior, we wish to reason about the probability that an agent takes a given action at a certain time. Previous work combining temporal and probabilistic reasoning has made either independence or Markov assumptions. This work introduces Annotated Probabilistic Temporal (APT) logic which makes neither assumption. Statements in APT logic consist of rules of the form "Formula G becomes true with a probability [L,U] within T time units after formula F becomes true'' and can be written by experts or extracted automatically. We explore the problem of entailment - finding the probability that an agent performs a given action at a certain time based on such a model. We study this problem's complexity and develop a sound, but incomplete fixpoint operator as a heuristic - implementing it and testing it on automatically generated models from several datasets. Second, agent behavior often results in "observations'' at geospatial locations that imply the existence of other, unobserved, locations we wish to find ("partners"). In this dissertation, we formalize this notion with "geospatial abduction problems" (GAPs). GAPs try to infer a set of partner locations for a set of observations and a model representing the relationship between observations and partners for a given agent. This dissertation presents exact and approximate algorithms for solving GAPs as well as an implemented software package for addressing these problems called SCARE (the Spatio-Cultural Abductive Reasoning Engine). We tested SCARE on counter-insurgency data from Iraq and obtained good results. We then provide an adversarial extension to GAPs as follows: given a fixed set of observations, if an adversary has probabilistic knowledge of how an agent were to find a corresponding set of partners, he would place the partners in locations that minimize the expected number of partners found by the agent. We examine this problem, along with its complement by studying their computational complexity, developing algorithms, and implementing approximation schemes. We also introduce a class of problems called geospatial optimization problems (GOPs). Here the agent has a set of actions that modify attributes of a geospatial region and he wishes to select a limited number of such actions (with respect to some budget and other constraints) in a manner that maximizes a benefit function. We study the complexity of this problem and develop exact methods. We then develop an approximation algorithm with a guarantee. For some real-world applications, such as epidemiology, there is an underlying diffusion process that also affects geospatial proprieties. We address this with social network optimization problems (SNOPs) where given a weighted, labeled, directed graph we seek to find a set of vertices, that if given some initial property, optimize an aggregate study with respect to such diffusion. We develop and implement a heuristic that obtains a guarantee for a large class of such problems

A study in the cognition of individuals’ identity: Solving the problem of singular cognition in object and agent tracking

This article compares the ability to track individuals lacking mental states with the ability to track intentional agents. It explains why reference to individuals raises the problem of explaining how cognitive agents track unique individuals and in what sense reference is based on procedures of perceptual-motor and epistemic tracking. We suggest applying the notion of singular-files from theories in perception and semantics to the problem of tracking intentional agents. In order to elucidate the nature of agent-files, three views of the relation between object- and agent-tracking are distinguished: the Independence, Deflationary and Organism-Dependence Views. The correct view is argued to be the latter, which states that perceptual and epistemic tracking of a unique human organism requires tracking both its spatio-temporal object-properties and its agent-properties

Between Sense and Sensibility: Declarative narrativisation of mental models as a basis and benchmark for visuo-spatial cognition and computation focussed collaborative cognitive systems

What lies between `\emph{sensing}' and `\emph{sensibility}'? In other words, what kind of cognitive processes mediate sensing capability, and the formation of sensible impressions ---e.g., abstractions, analogies, hypotheses and theory formation, beliefs and their revision, argument formation--- in domain-specific problem solving, or in regular activities of everyday living, working and simply going around in the environment? How can knowledge and reasoning about such capabilities, as exhibited by humans in particular problem contexts, be used as a model and benchmark for the development of collaborative cognitive (interaction) systems concerned with human assistance, assurance, and empowerment? We pose these questions in the context of a range of assistive technologies concerned with \emph{visuo-spatial perception and cognition} tasks encompassing aspects such as commonsense, creativity, and the application of specialist domain knowledge and problem-solving thought processes. Assistive technologies being considered include: (a) human activity interpretation; (b) high-level cognitive rovotics; (c) people-centred creative design in domains such as architecture & digital media creation, and (d) qualitative analyses geographic information systems. Computational narratives not only provide a rich cognitive basis, but they also serve as a benchmark of functional performance in our development of computational cognitive assistance systems. We posit that computational narrativisation pertaining to space, actions, and change provides a useful model of \emph{visual} and \emph{spatio-temporal thinking} within a wide-range of problem-solving tasks and application areas where collaborative cognitive systems could serve an assistive and empowering function.Comment: 5 pages, research statement summarising recent publication

Grounding Dynamic Spatial Relations for Embodied (Robot) Interaction

This paper presents a computational model of the processing of dynamic spatial relations occurring in an embodied robotic interaction setup. A complete system is introduced that allows autonomous robots to produce and interpret dynamic spatial phrases (in English) given an environment of moving objects. The model unites two separate research strands: computational cognitive semantics and on commonsense spatial representation and reasoning. The model for the first time demonstrates an integration of these different strands.Comment: in: Pham, D.-N. and Park, S.-B., editors, PRICAI 2014: Trends in Artificial Intelligence, volume 8862 of Lecture Notes in Computer Science, pages 958-971. Springe

Cognitive visual tracking and camera control

Cognitive visual tracking is the process of observing and understanding the behaviour of a moving person. This paper presents an efficient solution to extract, in real-time, high-level information from an observed scene, and generate the most appropriate commands for a set of pan-tilt-zoom (PTZ) cameras in a surveillance scenario. Such a high-level feedback control loop, which is the main novelty of our work, will serve to reduce uncertainties in the observed scene and to maximize the amount of information extracted from it. It is implemented with a distributed camera system using SQL tables as virtual communication channels, and Situation Graph Trees for knowledge representation, inference and high-level camera control. A set of experiments in a surveillance scenario show the effectiveness of our approach and its potential for real applications of cognitive vision

On Formal Methods for Collective Adaptive System Engineering. {Scalable Approximated, Spatial} Analysis Techniques. Extended Abstract

In this extended abstract a view on the role of Formal Methods in System Engineering is briefly presented. Then two examples of useful analysis techniques based on solid mathematical theories are discussed as well as the software tools which have been built for supporting such techniques. The first technique is Scalable Approximated Population DTMC Model-checking. The second one is Spatial Model-checking for Closure Spaces. Both techniques have been developed in the context of the EU funded project QUANTICOL.Comment: In Proceedings FORECAST 2016, arXiv:1607.0200