Internal Simulation of an Agent\u2019s Intentions

We present the Associative Self-Organizing Map (A-SOM) and propose that it could be used to predict an agent's intentions by internally simulating the behaviour likely to follow initial movements. The A-SOM is a neural network that develops a representation of its input space without supervision, while simultaneously learning to associate its activity with an arbitrary number of additional (possibly delayed) inputs. We argue that the A-SOM would be suitable for the prediction of the likely continuation of the perceived behaviour of an agent by learning to associate activity patterns over time, and thus a way to read its intentions

Dynamics of positrons during relativistic electron runaway

Sufficiently strong electric fields in plasmas can accelerate charged particles to relativistic energies. In this paper we describe the dynamics of positrons accelerated in such electric fields, and calculate the fraction of created positrons that become runaway accelerated, along with the amount of radiation that they emit. We derive an analytical formula that shows the relative importance of the different positron production processes, and show that above a certain threshold electric field the pair production by photons is lower than that by collisions. We furthermore present analytical and numerical solutions to the positron kinetic equation; these are applied to calculate the fraction of positrons that become accelerated or thermalized, which enters into rate equations that describe the evolution of the density of the slow and fast positron populations. Finally, to indicate operational parameters required for positron detection during runaway in tokamak discharges, we give expressions for the parameter dependencies of detected annihilation radiation compared to bremsstrahlung detected at an angle perpendicular to the direction of runaway acceleration. Using the full leading order pair production cross section, we demonstrate that previous related work has overestimated the collisional pair production by at least a factor of four

Influence of massive material injection on avalanche runaway generation during tokamak disruptions

In high-current tokamak devices such as ITER, a runaway avalanche can cause a large amplification of a seed electron population. We show that disruption mitigation by impurity injection may significantly increase the runaway avalanche growth rate in such devices. This effect originates from the increased number of target electrons available for the avalanche process in weakly ionized plasmas, which is only partially compensated by the increased friction force on fast electrons. We derive an expression for the avalanche growth rate in partially ionized plasmas and investigate the effects of impurity injection on the avalanche multiplication factor and on the final runaway current for ITER-like parameters. For impurity densities relevant for disruption mitigation, the maximum amplification of a runaway seed can be increased by tens of orders of magnitude compared to previous predictions. This motivates careful studies to determine the required densities and impurity species to obtain tolerable current quench parameters, as well as more detailed modeling of the runaway dynamics including transport effects.Comment: 6 pages, 2 figure

Effect of partially-screened nuclei on fast-electron dynamics

We analyze the dynamics of fast electrons in plasmas containing partially ionized impurity atoms, where the screening effect of bound electrons must be included. We derive analytical expressions for the deflection and slowing-down frequencies, and show that they are increased significantly compared to the results obtained with complete screening, already at sub-relativistic electron energies. Furthermore, we show that the modifications to the deflection and slowing down frequencies are of equal importance in describing the runaway current evolution. Our results greatly affect fast-electron dynamics and have important implications, e.g. for the efficacy of mitigation strategies for runaway electrons in tokamak devices, and energy loss during relativistic breakdown in atmospheric discharges.Comment: 6 pages, 3 figures, fixed minor typo

Effect of Screened Nuclei on Fast Electron Beam Dynamics

In a plasma, particles can be accelerated to relativistic speeds by an electric eld. These relativistic particles are of importance to, for example, fusion research, where they pose a risk of damaging the walls of a reactor. In order to understand the dynamics of runaway particles, the Coulomb interaction between particles (\collisions") is a central concept. In this thesis we focus on the collisions between particles and partially ionized ions. The interaction strength depends on the incoming particle momentum: at low momentum the nucleus is screened by the bound electrons and the net ion charge will de ne the interaction strength; at high momentum the particle may penetrate the electron cloud around the nucleus and the relevant charge is then the nuclear charge. Since the collision cross section is proportional to the charge squared, this can be expected to have a signi cant impact on the runaway dynamics. In this thesis we investigate the energy dependence of screening. Starting from a quantum mechanical collision cross section, we derive the form of the Fokker{Planck collision operator appropriate for the many-body plasma system. When accurately accounting for screening, we nd that the collision rates can be signi cantly enhanced compared to the fully screened case, in particular at high momentum. Furthermore, we derive general forms of the high energy behavior of the collision frequencies, which hold regardless of the details of the model used to describe the electron cloud of the ion. Finally, we nd indications that the use of the Fokker{Planck operator might have to be improved by considering the Boltzmann operator, in order to take large-angle collisions into account

The current status of the simulation theory of cognition

It is proposed that thinking is simulated interaction with the environment. Three assumptions underlie this ‘simulation’ theory of cognitive function. Firstly, behaviour can be simulated in the sense that we can activate motor structures, as during a normal overt action, but suppress its execution. Secondly, perception can be simulated by internal activation of sensory cortex in a way that resembles its normal activation during perception of external stimuli. The third assumption (‘anticipation’) is that both overt and simulated actions can elicit perceptual simulation of their most probable consequences. A large body of evidence, mainly from neuroimaging studies, that supports these assumptions, is reviewed briefly. The theory is ontologically parsimonious and does not rely on standard cognitivist constructs such as internal models or representations. It is argued that the simulation approach can explain the relations between motor, sensory and cognitive functions and the appearance of an inner world. It also unifies and explains important features of a wide variety of cognitive phenomena such as memory and cognitive maps. Novel findings from recent developments in memory research on the similarity of imaging and memory and on the role of both prefrontal cortex and sensory cortex in declarative memory and working memory are predicted by the theory and provide striking support for it

Kinetic modeling of runaway-electron dynamics in partially ionized plasmas

An essential result of kinetic plasma physics is the runaway phenomenon, whereby a fraction of an electron population can be accelerated to relativistic energies. Such runaway electrons are formed in astrophysical settings, but are also of great practical relevance to fusion research. In the most developed fusion device, known as the tokamak, runaway electrons have the potential to cause severe damage to the first wall. Runaway-electron mitigation is therefore one of the critical issues in the design of a fusion power plant. In many situations, runaway electrons interact with partially ionized atoms. In particular, the currently envisaged mitigation method for tokamaks is to inject heavy atoms which collisionally dissipate the runaway beam before it can collide with the wall, or prevent it from forming at all. When the atoms are partially ionized, their bound electrons screen out a fraction of the atomic charge, which directly affects the collisional scattering rates. However, accurate expressions for these collisional scattering rates between energetic electrons and partially ionized atoms have not been available previously. In this thesis, we explore kinetic aspects of runaway dynamics in partially ionized plasmas. We derive collisional scattering rates using a quantum-mechanical treatment, and study the interaction between fast electrons and partially ionized atoms. We then apply these results to calculate the threshold field for runaway generation, as well as the production rate of runaway electrons via the avalanche and Dreicer mechanisms. We find that even if material injection increases the dissipation rates, it also enhances avalanche generation which could potentially aggravate the runaway problem. These results contribute to more accurate runaway-electron modeling and can lead to more effective mitigation schemes in the longer term

Representation recovers information

Early agreement within cognitive science on the topic of representation has now given way to a combination of positions. Some question the significance of representation in cognition. Others continue to argue in favor, but the case has not been demonstrated in any formal way. The present paper sets out a framework in which the value of representation-use can be mathematically measured, albeit in a broadly sensory context rather than a specifically cognitive one. Key to the approach is the use of Bayesian networks for modeling the distal dimension of sensory processes. More relevant to cognitive science is the theoretical result obtained, which is that a certain type of representational architecture is *necessary* for achievement of sensory efficiency. While exhibiting few of the characteristics of traditional, symbolic encoding, this architecture corresponds quite closely to the forms of embedded representation now being explored in some embedded/embodied approaches. It becomes meaningful to view that type of representation-use as a form of information recovery. A formal basis then exists for viewing representation not so much as the substrate of reasoning and thought, but rather as a general medium for efficient, interpretive processing

Projective simulation for artificial intelligence

We propose a model of a learning agent whose interaction with the environment is governed by a simulation-based projection, which allows the agent to project itself into future situations before it takes real action. Projective simulation is based on a random walk through a network of clips, which are elementary patches of episodic memory. The network of clips changes dynamically, both due to new perceptual input and due to certain compositional principles of the simulation process. During simulation, the clips are screened for specific features which trigger factual action of the agent. The scheme is different from other, computational, notions of simulation, and it provides a new element in an embodied cognitive science approach to intelligent action and learning. Our model provides a natural route for generalization to quantum-mechanical operation and connects the fields of reinforcement learning and quantum computation.Comment: 22 pages, 18 figures. Close to published version, with footnotes retaine

Memory consolidation in the cerebellar cortex

Several forms of learning, including classical conditioning of the eyeblink, depend upon the cerebellum. In examining mechanisms of eyeblink conditioning in rabbits, reversible inactivations of the control circuitry have begun to dissociate aspects of cerebellar cortical and nuclear function in memory consolidation. It was previously shown that post-training cerebellar cortical, but not nuclear, inactivations with the GABA(A) agonist muscimol prevented consolidation but these findings left open the question as to how final memory storage was partitioned across cortical and nuclear levels. Memory consolidation might be essentially cortical and directly disturbed by actions of the muscimol, or it might be nuclear, and sensitive to the raised excitability of the nuclear neurons following the loss of cortical inhibition. To resolve this question, we simultaneously inactivated cerebellar cortical lobule HVI and the anterior interpositus nucleus of rabbits during the post-training period, so protecting the nuclei from disinhibitory effects of cortical inactivation. Consolidation was impaired by these simultaneous inactivations. Because direct application of muscimol to the nuclei alone has no impact upon consolidation, we can conclude that post-training, consolidation processes and memory storage for eyeblink conditioning have critical cerebellar cortical components. The findings are consistent with a recent model that suggests the distribution of learning-related plasticity across cortical and nuclear levels is task-dependent. There can be transfer to nuclear or brainstem levels for control of high-frequency responses but learning with lower frequency response components, such as in eyeblink conditioning, remains mainly dependent upon cortical memory storage