244,687 research outputs found
Preservation of Semantic Properties during the Aggregation of Abstract Argumentation Frameworks
An abstract argumentation framework can be used to model the argumentative
stance of an agent at a high level of abstraction, by indicating for every pair
of arguments that is being considered in a debate whether the first attacks the
second. When modelling a group of agents engaged in a debate, we may wish to
aggregate their individual argumentation frameworks to obtain a single such
framework that reflects the consensus of the group. Even when agents disagree
on many details, there may well be high-level agreement on important semantic
properties, such as the acceptability of a given argument. Using techniques
from social choice theory, we analyse under what circumstances such semantic
properties agreed upon by the individual agents can be preserved under
aggregation.Comment: In Proceedings TARK 2017, arXiv:1707.0825
The neural representation of mental beliefs held by two agents
Neuroimaging research has demonstrated that mentalizing about false beliefs held by other people recruits the temporo-parietal junction (TPJ). However, earlier work was limited to a single agent that held a false belief. We investigated the effect of two agents that held similar or mixed false and/or true beliefs. Participants saw animated stories with two smurfs holding true or false beliefs (Story phase). At the end of each trial, they were requested to take the perspective of the self or one of the smurfs (Question phase). We predicted that an increasing number of smurfs holding a false belief would increase activation in the TPJ when participants have to report the belief of the smurf, because the incongruent belief should have a stronger influence if it is held by two compared with one agent. This prediction was confirmed as activation in the TPJ during the Story and Question phase increased when more smurfs held a false belief. Taking the perspective of the self led to stronger activation of the TPJ in the two conditions that involved a true belief and weakest activation in the condition of two false beliefs. These data suggest that activation in TPJ depends on the perspective participants take, and that the number of agents holding a false belief influences activation in the TPJ only when taking the agent's perspective
A knowledge representation meta-model for rule-based modelling of signalling networks
The study of cellular signalling pathways and their deregulation in disease
states, such as cancer, is a large and extremely complex task. Indeed, these
systems involve many parts and processes but are studied piecewise and their
literatures and data are consequently fragmented, distributed and sometimes--at
least apparently--inconsistent. This makes it extremely difficult to build
significant explanatory models with the result that effects in these systems
that are brought about by many interacting factors are poorly understood.
The rule-based approach to modelling has shown some promise for the
representation of the highly combinatorial systems typically found in
signalling where many of the proteins are composed of multiple binding domains,
capable of simultaneous interactions, and/or peptide motifs controlled by
post-translational modifications. However, the rule-based approach requires
highly detailed information about the precise conditions for each and every
interaction which is rarely available from any one single source. Rather, these
conditions must be painstakingly inferred and curated, by hand, from
information contained in many papers--each of which contains only part of the
story.
In this paper, we introduce a graph-based meta-model, attuned to the
representation of cellular signalling networks, which aims to ease this massive
cognitive burden on the rule-based curation process. This meta-model is a
generalization of that used by Kappa and BNGL which allows for the flexible
representation of knowledge at various levels of granularity. In particular, it
allows us to deal with information which has either too little, or too much,
detail with respect to the strict rule-based meta-model. Our approach provides
a basis for the gradual aggregation of fragmented biological knowledge
extracted from the literature into an instance of the meta-model from which we
can define an automated translation into executable Kappa programs.Comment: In Proceedings DCM 2015, arXiv:1603.0053
Arguing Using Opponent Models
Peer reviewedPostprin
Deep Reinforcement Learning from Self-Play in Imperfect-Information Games
Many real-world applications can be described as large-scale games of
imperfect information. To deal with these challenging domains, prior work has
focused on computing Nash equilibria in a handcrafted abstraction of the
domain. In this paper we introduce the first scalable end-to-end approach to
learning approximate Nash equilibria without prior domain knowledge. Our method
combines fictitious self-play with deep reinforcement learning. When applied to
Leduc poker, Neural Fictitious Self-Play (NFSP) approached a Nash equilibrium,
whereas common reinforcement learning methods diverged. In Limit Texas Holdem,
a poker game of real-world scale, NFSP learnt a strategy that approached the
performance of state-of-the-art, superhuman algorithms based on significant
domain expertise.Comment: updated version, incorporating conference feedbac
Resilience of multi-robot systems to physical masquerade attacks
The advent of autonomous mobile multi-robot systems has driven innovation in both the industrial and defense sectors. The integration of such systems in safety-and security-critical applications has raised concern over their resilience to attack. In this work, we investigate the security problem of a stealthy adversary masquerading as a properly functioning agent. We show that conventional multi-agent pathfinding solutions are vulnerable to these physical masquerade attacks. Furthermore, we provide a constraint-based formulation of multi-agent pathfinding that yields multi-agent plans that are provably resilient to physical masquerade attacks. This formalization leverages inter-agent observations to facilitate introspective monitoring to guarantee resilience.Accepted manuscrip
Statistics of opinion domains of the majority-vote model on a square lattice
The existence of juxtaposed regions of distinct cultures in spite of the fact
that people's beliefs have a tendency to become more similar to each other's as
the individuals interact repeatedly is a puzzling phenomenon in the social
sciences. Here we study an extreme version of the frequency-dependent bias
model of social influence in which an individual adopts the opinion shared by
the majority of the members of its extended neighborhood, which includes the
individual itself. This is a variant of the majority-vote model in which the
individual retains its opinion in case there is a tie among the neighbors'
opinions. We assume that the individuals are fixed in the sites of a square
lattice of linear size and that they interact with their nearest neighbors
only.
Within a mean-field framework, we derive the equations of motion for the
density of individuals adopting a particular opinion in the single-site and
pair approximations. Although the single-site approximation predicts a single
opinion domain that takes over the entire lattice, the pair approximation
yields a qualitatively correct picture with the coexistence of different
opinion domains and a strong dependence on the initial conditions. Extensive
Monte Carlo simulations indicate the existence of a rich distribution of
opinion domains or clusters, the number of which grows with whereas the
size of the largest cluster grows with . The analysis of the sizes of
the opinion domains shows that they obey a power-law distribution for not too
large sizes but that they are exponentially distributed in the limit of very
large clusters. In addition, similarly to other well-known social influence
model -- Axelrod's model -- we found that these opinion domains are unstable to
the effect of a thermal-like noise
Computational Evidence that Self-regulation of Creativity is Good for Society
Excess individual creativity can be detrimental to society because creators
invest in unproven ideas at the expense of propagating proven ones. Moreover, a
proportion of individuals can benefit from creativity without being creative
themselves by copying creators. We hypothesized that (1) societies increase
their rate of cultural evolution by tempering the novelty-generating effects of
creativity with the novelty-preserving effects of imitation, and (2) this is
carried out by selectively rewarding and punishing creativity according to the
value of the individuals' creative outputs. We tested this using an agent-based
model of cultural evolution in which each agent self-regulated its
invention-to-imitation ratio as a function of the fitness of its cultural
outputs. In self-regulating societies, agents segregated into creators and
imitators. The mean fitness of cultural outputs was higher than in
non-self-regulating societies, and changes in diversity were rapider and more
pronounced. We discuss limitations and possible social implications of our
findings.Comment: 6 pages. arXiv admin note: substantial text overlap with
arXiv:1310.475
Modelling Adaptation through Social Allostasis: Modulating the Effects of Social Touch with Oxytocin in Embodied Agents
Social allostasis is a mechanism of adaptation that permits individuals to dynamically adapt their physiology to changing physical and social conditions. Oxytocin (OT) is widely considered to be one of the hormones that drives and adapts social behaviours. While its precise effects remain unclear, two areas where OT may promote adaptation are by affecting social salience, and affecting internal responses of performing social behaviours. Working towards a model of dynamic adaptation through social allostasis in simulated embodied agents, and extending our previous work studying OT-inspired modulation of social salience, we present a model and experiments that investigate the effects and adaptive value of allostatic processes based on hormonal (OT) modulation of affective elements of a social behaviour. In particular, we investigate and test the effects and adaptive value of modulating the degree of satisfaction of tactile contact in a social motivation context in a small simulated agent society across different environmental challenges (related to availability of food) and effects of OT modulation of social salience as a motivational incentive. Our results show that the effects of these modulatory mechanisms have different (positive or negative) adaptive value across different groups and under different environmental circumstance in a way that supports the context-dependent nature of OT, put forward by the interactionist approach to OT modulation in biological agents. In terms of simulation models, this means that OT modulation of the mechanisms that we have described should be context-dependent in order to maximise viability of our socially adaptive agents, illustrating the relevance of social allostasis mechanisms.Peer reviewedFinal Published versio
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