A MULTIAGENT BASED SIMULATION FRAMEWORK FOR MAMMALIAN BEHAVIOUR

The primary aim of mammalian behaviour simulation is to allow “behaviourologists” to extend their current knowledge without needing to resort to expensive and intrusive real life experimentation. A useful mechanism for realising mammalian behaviour simulation is provided by the idea of Multi-Agent Based Simulation (MABS) where each "player" in a simulation is represented by an agent with a particular set of features or capabilities. This thesis proposed the Mammalian Behaviour MABS (MBMABS) framework. The fundamental idea presented in this thesis is that each mammal featured in the simulation can be modelled as an agent that has a set of desires and a set of behaviours. The desires may be static, in that they do not change for the duration of a simulation, or dynamic in that they change with time during a simulation (influenced by some internal or external event). In the work presented behaviours are modelled using the concept of a behaviour graph comprised of vertices representing states and edges indicating possible state changes. State changes occur as a result of an agent completing some self-appointed task or as a result of some external event. Each state has one or more predefined potential follow on states. Where there is more than one follow on state selection is made according to a weighted random selection process. The weightings are derived dynamically according to individual agent’s desires. A particular novel element of the proposed approach is that it features a degree of randomness, agents will not behave in the same manner on each occasion that a simulation is run. The operation of the MBMABS framework is illustrated in this thesis using a collection of mouse behaviour case studies, in which real mice are represented as individual agents. The reported evaluation of the case studies demonstrated that the proposed framework readily supports rodent behaviour simulations. The reported evaluation also indicated that the proposed simulation framework readily allows users to observe the behaviour of the simulated entities. More specifically the evaluation of the simulations was conducted by: (i) comparing the operation of the proposed MBMABS with video data, (ii) visual observation and (iii) reference to domain experts. The MBMABS experiments conducted using video data successfully indicated that there was a similarity in the behaviour of mouse agents operating within the framework and real life mice (as recorded using video data). Mouse behaviour such as thigmotaxis and nest site selection was observed in both the simulation and video. The evaluation also indicated that the MBMABS framework readily supported the addition of states and desires. However, is was also noted that: (i) as the number of states increased the behaviour graph became more complex and difficult to visualise and (ii) as the number of agents interacting with the behaviour graph increased, the performance of the proposed framework was also affected in the sense that it required more resources to operate optimally

The Origin of Phenotypic Heterogeneity in a Clonal Cell Population In Vitro

BACKGROUND: The spontaneous emergence of phenotypic heterogeneity in clonal populations of mammalian cells in vitro is a rule rather than an exception. We consider two simple, mutually non-exclusive models that explain the generation of diverse cell types in a homogeneous population. In the first model, the phenotypic switch is the consequence of extrinsic factors. Initially identical cells may become different because they encounter different local environments that induce adaptive responses. According to the second model, the phenotypic switch is intrinsic to the cells that may occur even in homogeneous environments. PRINCIPAL FINDINGS: We have investigated the “extrinsic” and the “intrinsic” mechanisms using computer simulations and experimentation. First, we simulated in silico the emergence of two cell types in a clonal cell population using a multiagent model. Both mechanisms produced stable phenotypic heterogeneity, but the distribution of the cell types was different. The “intrinsic” model predicted an even distribution of the rare phenotype cells, while in the “extrinsic” model these cells formed small clusters. The key predictions of the two models were confronted with the results obtained experimentally using a myogenic cell line. CONCLUSIONS: The observations emphasize the importance of the “ecological” context and suggest that, consistently with the “extrinsic” model, local stochastic interactions between phenotypically identical cells play a key role in the initiation of phenotypic switch. Nevertheless, the “intrinsic” model also shows some other aspects of reality: The phenotypic switch is not triggered exclusively by the local environmental variations, but also depends to some extent on the phenotypic intrinsic robustness of the cells

A conceptual affective design framework for the use of emotions in computer game design

The purpose of this strategy of inquiry is to understand how emotions influence gameplay and to review contemporary techniques to design for them in the aim of devising a model that brings current disparate parts of the game design process together. Emotions sit at the heart of a game player’s level of engagement. They are evoked across many of the components that facilitate gameplay including the interface, the player’s avatar, non-player characters and narrative. Understanding the role of emotion in creating truly immersive and believable environments is critical for game designers. After discussing a taxonomy of emotion, this paper will present a systematic literature review of designing for emotion in computer games. Following this, a conceptual framework for affective design is offered as a guide for the future of computer game design

Applying biological paradigms to emerge behaviour in RoboCup Rescue team

This paper presents a hybrid behaviour process for performing collaborative tasks and coordination capabilities in a rescue team. RoboCup Rescue simulator and its associated international competition are used as the testbed for our proposal. Unlike other published work in this field one of our main concerns is having good results on RoboCup Rescue championships by emerging behaviour in agents using a biological paradigm. The benefit comes from the hierarchic and parallel organisation of the mammalian brain. In our behaviour process, Artificial Neural Networks are used in order to make agents capable of learning information from the environment. This allows agents to improve several algorithms like their Path Finding Algorithm to find the shortest path between two points. Also, we aim to filter the most important messages that arise from the environment, to make the right choice on the best path planning among many alternatives, in a short time. A policy action was implemented using Kohonen's network, Dijkstra's and D* algorithm. This policy has achieved good results in our tests, getting our team classified for RoboCup Rescue Simulation League 2005