Optimizing Associative Information Transfer within Content-addressable Memory

Original article can be found at: http://www.oldcitypublishing.com/IJUC/IJUC.htmlPeer reviewe

Security Challenges for Swarm Robotics

Swarm robotics is a relatively new technology that is being explored for its potential use in a variety of dierent applications and environments. Previous emerging technologies have often overlooked security until later developmen- tal stages, when it has had to be undesirably (and sometimes expensively) retrotted. We identify a number of security challenges for swarm robotics and argue that now is the right time to address these issues and seek solu- tions. We also identify several idiosyncrasies of swarm robotics that present some unique security challenges. In particular, swarms of robots potentially employ dierent types of communication channels; have special concepts of identity; and exhibit adaptive emergent behaviour which could be modied by an intruder. Addressing these issues now will prevent undesirable conse- quences for many applications of this type of technology

Modelización Basada en Agentes aplicada a sociedades cazadoras recolectoras patagónicas

Jornadas de Jóvenes en Investigación Arqueológica, JIA (3as : 5-7 de mayo 2010 : Universitat Autònoma de Barcelona). Sesión 4. Cultura material y transdisciplinariedad en la investigación arqueológica de Latinoamérica.En este trabajo se presenta una propuesta metodológica basada en la simulación de poblaciones artificiales con el fin de abordar el análisis de la conformación de los límites étnicos de poblaciones patagónicas. La aplicación de modelos basados en sociedades artificiales es utilizada para comprender y explicar las sociedades reales. La aplicación de esta metodología supone que el comportamiento individual sigue reglas específicas, esperando que la sociedad concebida exhiba algunas propiedades particulares o regularidades estructurales. El modelo computacional propuesto no es una forma de validar teorías, es una herramienta que permite rastrear las implicaciones de las hipótesis con el fin de detectar donde se encuentra inconsistencias y contradicciones y así construir nuevas estructuras teóricas de explicación de los datos empíricos. La articulación de datos etnográficos sobre los grupos patagónicos históricos junto a las diferentes teorías de formación cazadora recolectora, constituyen el cuerpo básico sobre el que se intenta derivar la simulación de la emergencia de la etnicidad y las fronteras entre las sociedades patagónicas.This work proposes an artificial simulation methodology based on artificial social simulation in order to approach the development of ethnic boundaries in Patagonian populations. The application of models based on artificial societies is used to understand and explain the real societies. This methodology assumes that individual behavior follows specific rules, hoping that the designed society exhibits some particular properties or structural regularities. The proposed computational model is not a way to validate theories, is a tool to trace the implications of the scenarios in order to detect inconsistencies and contradictions and build new structures and theoretical explanation of empirical data. The articulation of ethnographic and historical data from Patagonian groups with different theories of hunter-gatherer, is the statement from which it attempts to derive the simulation of the emergence of ethnicity and boundaries between patagonic-societies.Aquest treball presenta una proposta metodològica basada en la simulació de poblacions artificials per tal d'abordar l'anàlisi de la conformació dels límits ètnics de poblacions patagòniques. L'aplicació de models basats en societats artificials és utilitzada per a comprendre i explicar les societats reals. L'aplicació d'aquesta metodologia suposa que el comportament individual segueix regles específiques, esperant que la societat concebuda que es mostri algunes propietats particulars o regularitats estructurals. El model computacional proposat no és una forma de validar teories, és una eina que permet rastrejar les implicacions de les hipòtesis per tal de detectar on hi ha inconsistències i contradiccions i així construir noves estructures teòriques d'explicació de les dades empíriques. L'articulació de dades etnogràfics sobre els grups patagònics històrics al costat de les diferents teories de formació caçadora-recol·lectora, constitueixen el cos bàsic sobre el qual s'intenta derivar la simulació de l'emergència de l'etnicitat i les fronteres entre les societats patagòniques

Digested Information as an Information Theoretic Motivation for Social Interaction

Within a universal agent-world interaction framework, based on Information Theory and Causal Bayesian Networks, we demonstrate how every agent that needs to acquire relevant information in regard to its strategy selection will automatically inject part of this information back into the environment. We introduce the concept of 'Digested Information' which both quantifies, and explains this phenomenon. Based on the properties of digested information, especially the high density of relevant information in other agents actions, we outline how this could motivate the development of low level social interaction mechanisms, such as the ability to detect other agents.Information Theory, Collective Behaviour, Inadvertent Social Information, Infotaxis, Digested Information, Bayesian Update

Gliders2d: Source Code Base for RoboCup 2D Soccer Simulation League

We describe Gliders2d, a base code release for Gliders, a soccer simulation team which won the RoboCup Soccer 2D Simulation League in 2016. We trace six evolutionary steps, each of which is encapsulated in a sequential change of the released code, from v1.1 to v1.6, starting from agent2d-3.1.1 (set as the baseline v1.0). These changes improve performance by adjusting the agents' stamina management, their pressing behaviour and the action-selection mechanism, as well as their positional choice in both attack and defense, and enabling riskier passes. The resultant behaviour, which is sufficiently generic to be applicable to physical robot teams, increases the players' mobility and achieves a better control of the field. The last presented version, Gliders2d-v1.6, approaches the strength of Gliders2013, and outperforms agent2d-3.1.1 by four goals per game on average. The sequential improvements demonstrate how the methodology of human-based evolutionary computation can markedly boost the overall performance with even a small number of controlled steps.Comment: 12 pages, 1 figure, Gliders2d code releas

How can bottom-up information shape learning of top-down attention-control skills?

How does bottom-up information affect the development of top-down attentional control skills during the learning of visuomotor tasks? Why is the eye fovea so small? Strong evidence supports the idea that in humans foveation is mainly guided by task-specific skills, but how these are learned is still an important open problem. We designed and implemented a simulated neural eye-arm coordination model to study the development of attention control in a search-and-reach task involving simple coloured stimuli. The model is endowed with a hard-wired bottom-up attention saliency map and a top-down attention component which acquires task-specific knowledge on potential gaze targets and their spatial relations. This architecture achieves high performance very fast. To explain this result, we argue that: (a) the interaction between bottom-up and top-down mechanisms supports the development of task-specific attention control skills by allowing an efficient exploration of potentially useful gaze targets; (b) bottom-up mechanisms boast the exploitation of the initial limited task-specific knowledge by actively selecting areas where it can be suitably applied; (c) bottom-up processes shape objects representation, their value, and their roles (these can change during learning, e.g. distractors can become useful attentional cues); (d) increasing the size of the fovea alleviates perceptual aliasing, but at the same time increases input processing costs and the number of trials required to learn. Overall, the results indicate that bottom-up attention mechanisms can play a relevant role in attention control, especially during the acquisition of new task-specific skills, but also during task performance

Empowerment for Continuous Agent-Environment Systems

This paper develops generalizations of empowerment to continuous states. Empowerment is a recently introduced information-theoretic quantity motivated by hypotheses about the efficiency of the sensorimotor loop in biological organisms, but also from considerations stemming from curiosity-driven learning. Empowemerment measures, for agent-environment systems with stochastic transitions, how much influence an agent has on its environment, but only that influence that can be sensed by the agent sensors. It is an information-theoretic generalization of joint controllability (influence on environment) and observability (measurement by sensors) of the environment by the agent, both controllability and observability being usually defined in control theory as the dimensionality of the control/observation spaces. Earlier work has shown that empowerment has various interesting and relevant properties, e.g., it allows us to identify salient states using only the dynamics, and it can act as intrinsic reward without requiring an external reward. However, in this previous work empowerment was limited to the case of small-scale and discrete domains and furthermore state transition probabilities were assumed to be known. The goal of this paper is to extend empowerment to the significantly more important and relevant case of continuous vector-valued state spaces and initially unknown state transition probabilities. The continuous state space is addressed by Monte-Carlo approximation; the unknown transitions are addressed by model learning and prediction for which we apply Gaussian processes regression with iterated forecasting. In a number of well-known continuous control tasks we examine the dynamics induced by empowerment and include an application to exploration and online model learning

Scale-free features in collective robot foraging

In many complex systems observed in nature, properties such as scalability, adaptivity, or rapid information exchange are often accompanied by the presence of features that are scale-free, i.e., that have no characteristic scale. Following this observation, we investigate the existence of scale-free features in artificial collective systems using simulated robot swarms. We implement a large-scale swarm performing the complex task of collective foraging, and demonstrate that several space and time features of the simulated swarm-such as number of communication links or time spent in resting state-spontaneously approach the scale-free property with moderate to strong statistical plausibility. Furthermore, we report strong correlations between the latter observation and swarm performance in terms of the number of retrieved items

Self-Motivated Composition of Strategic Action Policies

In the last 50 years computers have made dramatic progress in their capabilities, but at the same time their failings have demonstrated that we, as designers, do not yet understand the nature of intelligence. Chess playing, for example, was long offered up as an example of the unassailability of the human mind to Artificial Intelligence, but now a chess engine on a smartphone can beat a grandmaster. Yet, at the same time, computers struggle to beat amateur players in simpler games, such as Stratego, where sheer processing power cannot substitute for a lack of deeper understanding. The task of developing that deeper understanding is overwhelming, and has previously been underestimated. There are many threads and all must be investigated. This dissertation explores one of those threads, namely asking the question “How might an artificial agent decide on a sensible course of action, without being told what to do?”. To this end, this research builds upon empowerment, a universal utility which provides an entirely general method for allowing an agent to measure the preferability of one state over another. Empowerment requires no explicit goals, and instead favours states that maximise an agent’s control over its environment. Several extensions to the empowerment framework are proposed, which drastically increase the array of scenarios to which it can be applied, and allow it to evaluate actions in addition to states. These extensions are motivated by concepts such as bounded rationality, sub-goals, and anticipated future utility. In addition, the novel concept of strategic affinity is proposed as a general method for measuring the strategic similarity between two (or more) potential sequences of actions. It does this in a general fashion, by examining how similar the distribution of future possible states would be in the case of enacting either sequence. This allows an agent to group action sequences, even in an unknown task space, into ‘strategies’. Strategic affinity is combined with the empowerment extensions to form soft-horizon empowerment, which is capable of composing action policies in a variety of unknown scenarios. A Pac-Man-inspired prey game and the Gambler’s Problem are used to demonstrate this selfmotivated action selection, and a Sokoban inspired box-pushing scenario is used to highlight the capability to pick strategically diverse actions. The culmination of this is that soft-horizon empowerment demonstrates a variety of ‘intuitive’ behaviours, which are not dissimilar to what we might expect a human to try. This line of thinking demonstrates compelling results, and it is suggested there are a couple of avenues for immediate further research. One of the most promising of these would be applying the self-motivated methodology and strategic affinity method to a wider range of scenarios, with a view to developing improved heuristic approximations that generate similar results. A goal of replicating similar results, whilst reducing the computational overhead, could help drive an improved understanding of how we may get closer to replicating a human-like approach