Computational Theory of Mind for Human-Agent Coordination

In everyday life, people often depend on their theory of mind, i.e., their ability to reason about unobservable mental content of others to understand, explain, and predict their behaviour. Many agent-based models have been designed to develop computational theory of mind and analyze its effectiveness in various tasks and settings. However, most existing models are not generic (e.g., only applied in a given setting), not feasible (e.g., require too much information to be processed), or not human-inspired (e.g., do not capture the behavioral heuristics of humans). This hinders their applicability in many settings. Accordingly, we propose a new computational theory of mind, which captures the human decision heuristics of reasoning by abstracting individual beliefs about others. We specifically study computational affinity and show how it can be used in tandem with theory of mind reasoning when designing agent models for human-agent negotiation. We perform two-agent simulations to analyze the role of affinity in getting to agreements when there is a bound on the time to be spent for negotiating. Our results suggest that modeling affinity can ease the negotiation process by decreasing the number of rounds needed for an agreement as well as yield a higher benefit for agents with theory of mind reasoning.</p

Deconstructing the beast: Contemporary representations and discourses on the nature of animals in urban Britain.

This thesis explores contemporary representations and discourses on the nature of animals through the development and investigation of the psychodynamics of dehumanisation. Psychodynamics integrates the discursive structuring of knowledge with the psychoanalytical defence mechanisms of projection and introjection. Hollway's (1989) theory of psychodynamic investment is applied here to account for the reproduction of species-differentiated beliefs and behaviours. This provides the parameters for a model of dehumanisation which is the referent for the empirical exploration. The methodological approach employed centres on triangulation. Semi-structured interviews with vegetarians and non-vegetarians are discourse analysed to reveal patterns of naturalisation in the production of social meanings and the participation in social practices. The analysis reveals the content of the psychodynamic investments in three hegemonic discourses: nonhuman animals as Objects, Referents and Utensils. In order to explore representations of the referent, 'beast', newspaper articles are semiologically content analysed. This analysis identifies aspects of human experience which are projected onto nonhuman animal representations, through anthropomorphism and zoomorphism. To explore one example of these metaphors, the fantasy 'beast', a semiotic analysis of narrative was conducted on Wilderness, the book and derived drama, further articulating the symbolic dimension of irresolute species boundaries in a modem twist on the lycanthropic myth. The model of dehumanisation is developed to interpret the analysis of the texts. Deconstruction of these texts provide evidence for the anthropocentric, anthropomorphic and zoomorphic dialectics of self:other reflected in the violent construction of human versus nonhuman animal identity. The synthesised model of dehumanisation illustrates both the human desire to acknowledge and oppress nonhuman animals and the ubiquitous 'beast within', as part of the psychodynamic construction of subjectivity. In conclusion, the deconstructed 'beast' is revealed as a modern-day ideological chimera which signifies the ambivalence of humans' understanding of themselves as animals

Evolutionary and Eco-Evolutionary Stability in Aposematic Prey Populations

The term aposematism or "warning colouration" (as first characterised by Alfred Russel Wallace in 1877) describes the process by which defended organisms (animals or plants) advertise their unprofitability to potential predators to gain selective advantage. The first part of the thesis explores the relationship between evolutionarily stable levels of signalling and defence within the context of a game-theoretical, prey-predator setup in which the prey population consists of a single type. While it is implicitly assumed that the prey population is large enough to be considered effectively infinite the evolution of prey traits is also explored for intermediate-sized populations within the context of genetic algorithm approach. In the later chapters considerable effort is devoted to extending the mentioned predator-prey description to systems in which the prey population consists of two types, including a model and a mimic. This modification leads us naturally into the celebrated adaptive mechanism named after Henry Walter Bates, Batesian mimicry. In Batesian mimicry complexes individuals from a palatable (mimic) species resemble individuals from an unpalatable (model) species to gain protection against predators. While there is ample empirical evidence to suggest that individuals from one species may gain selective advantage by resembling individuals from another, the mathematical modelling of Batesian mimicry is rather limited. We predict that models and mimics can co-exist along a continuum of solutions (representing the conspicuousness, noxiousness, and average mimicto-model proportion) that are both ecologically and locally evolutionarily stable. We establish a number of novel results that confirm both common sense intuition and a considerable body of related works

Brain-Computer Interface

Brain-computer interfacing (BCI) with the use of advanced artificial intelligence identification is a rapidly growing new technology that allows a silently commanding brain to manipulate devices ranging from smartphones to advanced articulated robotic arms when physical control is not possible. BCI can be viewed as a collaboration between the brain and a device via the direct passage of electrical signals from neurons to an external system. The book provides a comprehensive summary of conventional and novel methods for processing brain signals. The chapters cover a range of topics including noninvasive and invasive signal acquisition, signal processing methods, deep learning approaches, and implementation of BCI in experimental problems