The ingredients of an exosomatic cognitive map: isovists, agents and axial lines?

There is some evidence that an axial map, as used in space syntax, may be related to an underlying cognitive map in humans. However, the axial map is derived strictly from the mathematical configuration of space rather than any property of people. Hence there is a question of how a person might have embedded such a map. In this paper we report the results of several experiments which aim to improve the correlation between agent and pedestrian movement.We use a database of external occlusion points derived from isovists constructed throughout the system to provide a lookup table for agents to guide their movement. Since the table is external to the agents, we refer to the visual architecture as exosomatic. The results do improve on previous studies, but are still far from a good simulation of pedestrian movement. However, there is a philosophically important outcome from the experiments. When the agents are tuned to best performance, their movement patterns correspond to the axial structure of the system. This can be shown to be a mathematical result of their movement strategy; that is, the manifestation of movement, or the `memory' of an agent experiment, relates to the combination of the internal structure of the agent and its engagement with the environment in the form of an axial map. There are two unresolved steps from the relationship between individual and environment to human cognition: one, it cannot be shown that people do actually use occlusion points for movement, and two, even if they were to, it cannot be shown that they would use the resultant axial structure for higher level navigation decisions. Nevertheless, our results do provide evidence for a link between the individual and the axial map through embodiment of an agent-environment system, and our theory provides a mechanism for a link between the embodied map and preconditions for cognitive structure, which may in turn provide a basis for the future research into the means by which space syntax may be related to spatial cognition

Computing the entropy of user navigation in the web

Navigation through the web, colloquially known as "surfing", is one of the main activities of users during web interaction. When users follow a navigation trail they often tend to get disoriented in terms of the goals of their original query and thus the discovery of typical user trails could be useful in providing navigation assistance. Herein, we give a theoretical underpinning of user navigation in terms of the entropy of an underlying Markov chain modelling the web topology. We present a novel method for online incremental computation of the entropy and a large deviation result regarding the length of a trail to realize the said entropy. We provide an error analysis for our estimation of the entropy in terms of the divergence between the empirical and actual probabilities. We then indicate applications of our algorithm in the area of web data mining. Finally, we present an extension of our technique to higher-order Markov chains by a suitable reduction of a higher-order Markov chain model to a first-order one

Grid Cell Hexagonal Patterns Formed by Fast Self-Organized Learning within Entorhinal Cortex

Grid cells in the dorsal segment of the medial entorhinal cortex (dMEC) show remarkable hexagonal activity patterns, at multiple spatial scales, during spatial navigation. How these hexagonal patterns arise has excited intense interest. It has previously been shown how a selforganizing map can convert firing patterns across entorhinal grid cells into hippocampal place cells that are capable of representing much larger spatial scales. Can grid cell firing fields also arise during navigation through learning within a self-organizing map? A neural model is proposed that converts path integration signals into hexagonal grid cell patterns of multiple scales. This GRID model creates only grid cell patterns with the observed hexagonal structure, predicts how these hexagonal patterns can be learned from experience, and can process biologically plausible neural input and output signals during navigation. These results support a unified computational framework for explaining how entorhinal-hippocampal interactions support spatial navigation.CELEST, a National Science Foundation Science of Learning Center (SBE-0354378); SyNAPSE program of Defense Advanced Research Projects Agency (HR00ll-09-3-0001, HR0011-09-C-0011

Automatic goal allocation for a planetary rover with DSmT

In this chapter, we propose an approach for assigning aninterest level to the goals of a planetary rover. Assigning an interest level to goals, allows the rover to autonomously transform and reallocate the goals. The interest level is defined by data-fusing payload and navigation information. The fusion yields an 'interest map',that quantifies the level of interest of each area around the rover. In this way the planner can choose the most interesting scientific objectives to be analysed, with limited human intervention, and reallocates its goals autonomously. The Dezert-Smarandache Theory of Plausible and Paradoxical Reasoning was used for information fusion: this theory allows dealing with vague and conflicting data. In particular, it allows us to directly model the behaviour of the scientists that have to evaluate the relevance of a particular set of goals. This chaptershows an application of the proposed approach to the generation of a reliable interest map

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available