Cognitive and affective motivation in conceptual modelling

A proposal is presented towards the extension of conceptual models of information systems, in order to allow specification and simulation of the behaviour of agents with an adequate degree of realism. Our method is mainly based on rules to infer the goals of agents from situations holding at given states. In this paper, we argue that the rules should take into account both cognitive and affective characteristics, as can be conveyed, for the various agents, by their individual profiles and current internal states. Such characteristics should also influence the choice of strategies to handle goal interferences in multi-goal/multi-agent environments.Keywords: Conceptual Modelling, Simulation, Multi-Agents, Affective Motivation, Goal Interference

Logical models for bounded reasoners

This dissertation aims at the logical modelling of aspects of human reasoning, informed by facts on the bounds of human cognition. We break down this challenge into three parts. In Part I, we discuss the place of logical systems for knowledge and belief in the Rationality Debate and we argue for systems that formalize an alternative picture of rationality -- one wherein empirical facts have a key role (Chapter 2). In Part II, we design logical models that encode explicitly the deductive reasoning of a single bounded agent and the variety of processes underlying it. This is achieved through the introduction of a dynamic, resource-sensitive, impossible-worlds semantics (Chapter 3). We then show that this type of semantics can be combined with plausibility models (Chapter 4) and that it can be instrumental in modelling the logical aspects of System 1 (“fast”) and System 2 (“slow”) cognitive processes (Chapter 5). In Part III, we move from single- to multi-agent frameworks. This unfolds in three directions: (a) the formation of beliefs about others (e.g. due to observation, memory, and communication), (b) the manipulation of beliefs (e.g. via acts of reasoning about oneself and others), and (c) the effect of the above on group reasoning. These questions are addressed, respectively, in Chapters 6, 7, and 8. We finally discuss directions for future work and we reflect on the contribution of the thesis as a whole (Chapter 9)

Dynamic Agent Based Modeling Using Bayesian Framework for Addressing Intelligence Adaptive Nuclear Nonproliferation Analysis

Realistically, no two nuclear proliferating or defensive entities are exactly identical; Agent Based Modeling (ABM) is a computational methodology addressing the uniqueness of those facilitating or preventing nuclear proliferation. The modular Bayesian ABM Nonproliferation Enterprise (BANE) tool has been developed at Texas A &M University for nuclear nonproliferation analysis. Entities engaged in nuclear proliferation cover a range of activities and fall within proliferating, defensive, and neutral agent classes. In BANE proliferating agents pursue nuclear weapons, or at least a latent nuclear weapons capability. Defensive nonproliferation agents seek to uncover, hinder, reverse, or dismantle any proliferation networks they discover. The vast majority of agents are neutral agents, of which only a small subset can significantly enable proliferation. BANE facilitates intelligent agent actions by employing entropy and mutual information for proliferation pathway determinations. Factors including technical success, resource expenditures, and detection probabilities are assessed by agents seeking optimal proliferation postures. Coupling ABM with Bayesian analysis is powerful from an omniscience limitation perspective. Bayesian analysis supports linking crucial knowledge and technology requirements into relationship networks for each proliferation category. With a Bayesian network, gaining information on proliferator actions in one category informs defensive agents where to expend limited counter-proliferation impeding capabilities. Correlating incomplete evidence for pattern recognition in BANE using Bayesian inference draws upon technical supply side proliferation linkages grounded in physics. Potential or current proliferator security, economic trajectory, or other factors modify demand drivers for undertaking proliferation. Using Bayesian inference the coupled demand and supply proliferation drivers are connected to create feedback interactions. Verification and some validation for BANE is performed using scenarios and historical case studies. Restrictive export controls, swings in global soft power affinity, and past proliferation program assessments for entities ranging from the Soviet Union to Iraq demonstrates BANE’s flexibility and applicability. As a newly developed tool, BANE has room for future contributions from computer science, engineering, and social scientists. Through BANE the framework exists for detailed nonproliferation expansion into broader weapons of mass effect analysis; since, nuclear proliferation is but one option for addressing international security concerns

Distributed on-line safety monitor based on safety assessment model and multi-agent system

On-line safety monitoring, i.e. the tasks of fault detection and diagnosis, alarm annunciation, and fault controlling, is essential in the operational phase of critical systems. Over the last 30 years, considerable work in this area has resulted in approaches that exploit models of the normal operational behaviour and failure of a system. Typically, these models incorporate on-line knowledge of the monitored system and enable qualitative and quantitative reasoning about the symptoms, causes and possible effects of faults. Recently, monitors that exploit knowledge derived from the application of off-line safety assessment techniques have been proposed. The motivation for that work has been the observation that, in current practice, vast amounts of knowledge derived from off-line safety assessments cease to be useful following the certification and deployment of a system. The concept is potentially very useful. However, the monitors that have been proposed so far are limited in their potential because they are monolithic and centralised, and therefore, have limited applicability in systems that have a distributed nature and incorporate large numbers of components that interact collaboratively in dynamic cooperative structures. On the other hand, recent work on multi-agent systems shows that the distributed reasoning paradigm could cope with the nature of such systems. This thesis proposes a distributed on-line safety monitor which combines the benefits of using knowledge derived from off-line safety assessments with the benefits of the distributed reasoning of the multi-agent system. The monitor consists of a multi-agent system incorporating a number of Belief-Desire-Intention (BDI) agents which operate on a distributed monitoring model that contains reference knowledge derived from off-line safety assessments. Guided by the monitoring model, agents are hierarchically deployed to observe the operational conditions across various levels of the hierarchy of the monitored system and work collaboratively to integrate and deliver safety monitoring tasks. These tasks include detection of parameter deviations, diagnosis of underlying causes, alarm annunciation and application of fault corrective measures. In order to avoid alarm avalanches and latent misleading alarms, the monitor optimises alarm annunciation by suppressing unimportant and false alarms, filtering spurious sensory measurements and incorporating helpful alarm information that is announced at the correct time. The thesis discusses the relevant literature, describes the structure and algorithms of the proposed monitor, and through experiments, it shows the benefits of the monitor which range from increasing the composability, extensibility and flexibility of on-line safety monitoring to ultimately developing an effective and cost-effective monitor. The approach is evaluated in two case studies and in the light of the results the thesis discusses and concludes both limitations and relative merits compared to earlier safety monitoring concepts

A distributed power-saving framework for LTE Het-Nets exploiting Almost Blank Subframes

Almost Blank Subframes (ABS) have been defined in LTE as a means to coordinate transmissions in heterogeneous networks (HetNets), composed of macro and micro eNodeBs: the macro issues ABS periods, and refrains from transmitting during ABSs, thus creating interference-free subframes for the micros. Micros report their capacity demands to the macro via the X2 interface, and the latter provisions the ABS period accordingly. Existing algorithms for ABS provisioning usually share resources proportionally among HetNet nodes in a long-term perspective (e.g., based on traffic forecast). We argue instead that this mechanism can be exploited to save power in the HetNet: in fact, during ABSs, the macro consumes less power, since it only transmits pilot signals. Dually, the micros may inhibit data transmission themselves in some subframes, and optimally decide when to do this based on knowledge of the ABS period. This allows us to define a power saving framework that works in the short term, modifying the ABS pattern at the fastest possible pace, serving the HetNet traffic at reduced power cost. Our framework is designed using only standard signaling. Simulations show that the algorithm consumes less power than its competitors, especially at low loads, and improves the UE QoS

Information handling: Concepts which emerged in practical situations and are analysed cybernetically

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University