6,020 research outputs found
Action and behavior: a free-energy formulation
We have previously tried to explain perceptual inference and learning under a free-energy principle that pursues Helmholtzâs agenda to understand the brain in terms of energy minimization. It is fairly easy to show that making inferences about the causes of sensory data can be cast as the minimization of a free-energy bound on the likelihood of sensory inputs, given an internal model of how they were caused. In this article, we consider what would happen if the data themselves were sampled to minimize this bound. It transpires that the ensuing active sampling or inference is mandated by ergodic arguments based on the very existence of adaptive agents. Furthermore, it accounts for many aspects of motor behavior; from retinal stabilization to goal-seeking. In particular, it suggests that motor control can be understood as fulfilling prior expectations about proprioceptive sensations. This formulation can explain why adaptive behavior emerges in biological agents and suggests a simple alternative to optimal control theory. We illustrate these points using simulations of oculomotor control and then apply to same principles to cued and goal-directed movements. In short, the free-energy formulation may provide an alternative perspective on the motor control that places it in an intimate relationship with perception
Collective states in social systems with interacting learning agents
We consider a social system of interacting heterogeneous agents with learning
abilities, a model close to Random Field Ising Models, where the random field
corresponds to the idiosyncratic willingness to pay. Given a fixed price,
agents decide repeatedly whether to buy or not a unit of a good, so as to
maximize their expected utilities. We show that the equilibrium reached by the
system depends on the nature of the information agents use to estimate their
expected utilities.Comment: 18 pages, 26 figure
Consensus-based approach to peer-to-peer electricity markets with product differentiation
With the sustained deployment of distributed generation capacities and the
more proactive role of consumers, power systems and their operation are
drifting away from a conventional top-down hierarchical structure. Electricity
market structures, however, have not yet embraced that evolution. Respecting
the high-dimensional, distributed and dynamic nature of modern power systems
would translate to designing peer-to-peer markets or, at least, to using such
an underlying decentralized structure to enable a bottom-up approach to future
electricity markets. A peer-to-peer market structure based on a Multi-Bilateral
Economic Dispatch (MBED) formulation is introduced, allowing for
multi-bilateral trading with product differentiation, for instance based on
consumer preferences. A Relaxed Consensus+Innovation (RCI) approach is
described to solve the MBED in fully decentralized manner. A set of realistic
case studies and their analysis allow us showing that such peer-to-peer market
structures can effectively yield market outcomes that are different from
centralized market structures and optimal in terms of respecting consumers
preferences while maximizing social welfare. Additionally, the RCI solving
approach allows for a fully decentralized market clearing which converges with
a negligible optimality gap, with a limited amount of information being shared.Comment: Accepted for publication in IEEE Transactions on Power System
Ontology-Mediated Probabilistic Model Checking: Extended Version
Probabilistic model checking (PMC) is a well-established method for the quantitative analysis of dynamic systems. On the other hand, description logics (DLs) provide a well-suited formalism to describe and reason about static knowledge, used in many areas to specify domain knowledge in an ontology. We investigate how such knowledge can be integrated into the PMC process, introducing ontology-mediated PMC. Specifically, we propose a formalism that links ontologies to dynamic behaviors specified by guarded commands, the de-facto standard input formalism for PMC tools such as Prism. Further, we present and implement a technique for their analysis relying on existing DL-reasoning and PMC tools. This way, we enable the application of standard PMC techniques to analyze knowledge-intensive systems. Our approach is implemented and evaluated on a multi-server system case study, where different DL-ontologies are used to provide specifications of different server platforms and situations the system is executed in
Contextualized Programs for Ontology-Mediated Probabilistic System Analysis
Modeling context-dependent systems for their analysis is challenging as verification tools usually rely on an input language close to imperative programming languages which need not support description of contexts well. We introduce the concept of contextualized programs where operational behaviors and context knowledge are modeled separately using domain-specific formalisms. For behaviors specified in stochastic guarded-command language and contextual knowledge given by OWL description logic ontologies, we develop a technique to efficiently incorporate contextual information into behavioral descriptions by reasoning about the ontology. We show how our presented concepts support and facilitate the quantitative analysis of context-dependent systems using probabilistic model checking. For this, we evaluate our implementation on a case study issuing a multi-server system
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