Robot learns sensorimotor relationships through exploration.
problem Autonomous acquisition of sensorimotor contingencies by robots.
method Developmental framework encoding predictive models of sensorimotor experience.
result Robot discovers the environment, objects, and visual field through internal encoding of sensorimotor contingencies.
Approach to develop visual perception in robots through sensorimotor interactions.
problem Developing autonomous perception in robots.
method Sensorimotor contingencies theory applied to robot exploration and learning.
result Captured sensorimotor regularities in a predictive model for visual field discovery.
Paper identifies invariant structures for object perception.
problem Understanding how agents perceive objects in dynamic environments.
method Sensorimotor Contingencies Theory inspired unsupervised predictive model.
result Agents can identify invariant structures in sensorimotor experiences.
New method allows a robot to perceive space dimensions without prior knowledge.
problem Limitation of previous methods in perceiving space dimensions with small movements.
method Non-linear dimension estimation method.
result Robots can now perceive space dimensions with larger movements.
Agents can learn spatial structure from raw sensorimotor experience.
problem Understanding spatial knowledge and its emergence in autonomous agents.
method A simple sensorimotor predictive scheme applied to various agents and exploration types.
result Agents can capture spatial topology and metric regularity without prior knowledge or supervision.
Agents learn spatial structure without supervision.
problem Understanding spatial knowledge in autonomous agents.
method Sensorimotor predictive scheme applied to various agents and exploration types.
result Agents can learn egocentric spatial structure without supervision.
New method infers human sensorimotor costs from behavior.
problem Inferring human sensorimotor costs from observed behavior.
method Inverse optimal control with signal-dependent noise.
result Recovering costs and benefits in sensorimotor behavior.
A neural network predicts a body image from sensorimotor data.
problem How to acquire a body image from sensorimotor data.
method A two-branches deconvolutional neural network trained on first-person images.
result The network can automatically isolate the visible arm from the environment.
Robots learn spatial perception from sensorimotor invariants.
problem Developing autonomous robots that perceive space without human intuition.
method Study how a robot's motor commands relate to changes in exteroceptive inputs to deduce its spatial configuration.
result Robots can learn the configuration space of their sensors, revealing a planar position and orientation.
NDPs embed dynamical systems into neural networks for efficient sensorimotor learning.
problem Training policies directly in raw action spaces limits scalability for continuous tasks.
method Embed dynamical systems into neural networks to learn robot behaviors via demonstrations.
result NDPs outperform prior methods in both imitation and reinforcement learning setups.
Researchers improved Minecraft game performance using imitation learning.
problem Achieving state-of-the-art performance in immersive environments like Minecraft.
method Applied imitation learning to Minecraft, optimizing network architecture, loss function, and data augmentation.
result Reported stronger results than previous experiments, reaching second place in a competition.
Derives time-averaged active inference from control principles.
problem Finite-horizon or discounted-surprise problems in active inference.
method Derives infinite-horizon, average-surprise active inference from optimal control principles.
result Unified objective functional for sensorimotor control.
One of the open challenges in designing robots that operate successfully in the unpredictable human environment is how to make them able to predict what actions they can perform on objects, and what their effects will be, i.e., the ability to perceive object affordances. Since modeling all the possible world interactio…
This work frames active inference through control as inference, offering robust control algorithms.
problem Active inference framework lacks practical sensorimotor control algorithms.
method Frame active inference through control as inference, presenting trajectory optimization as inference.
result AI may be framed as partially-observed CaI when the cost function is defined in observation states.
Enactive learning shows agents can learn from their environment, but limited by action choices.
problem Learning and interaction of autonomous agents in complex environments.
method Simulation of artificial agents in maze environments, comparing enactive learning to classical reinforcement learning.
result Enactive agents can learn to avoid unfavorable interactions but performance is limited by action choices.
Neural network approximates Bayesian decision-making parameters.
problem Analytical intractability of Bayesian decision-making in naturalistic tasks.
method Neural amortization of Bayesian actor model.
result Efficient gradient-based inference of Bayesian actor model parameters.
The study refines contingency matrices for complex stratification and braid group cohomology.
problem Combinatorics of contingency matrices and their applications.
method Refinement of complex stratification and study of braid group cohomology.
result Totally positive meta-matrix formed by contingency matrix sizes.
Paper introduces EEMs for pricing contingent claim returns.
problem Computing expected future prices of contingent claims.
method Dynamic change of measure approach to construct EEMs.
result EEMs provide physical and pricing expectations of contingent claim prices.
Contingency Training improves classifier accuracy and robustness against irrelevant variables.
problem Feature selection leaves irrelevant variables in high-dimensional datasets, reducing classifier performance.
method Subsampling and creating constraints to find proper feature importance weights.
result Contingency Training outperforms traditional training methods, especially with irrelevant variables.
The paper revisits and applies FTAP to life insurance and annuities pricing.
problem Non-arbitrage pricing of life contingent assets in dynamic markets.
method Revisit FTAP, use martingale theory, apply FTAP to life insurance and annuities, clarify assumptions.
result Valuation formula for life contingent assets including life insurance policies and annuities.
Study stability of contingent claim solutions under probabilistic perturbations.
problem Stability of solutions to discrete-time contingent-claim problems under uncertainty.
method Use Rockafellian perturbations to analyze stability of solutions.
result Establishes convergence of dual problems and shadow prices.
Paper proposes a method to estimate project cost contingency reserves considering various types of uncertainty.
problem Inaccurate estimation of project cost contingency reserves due to ignoring different types of uncertainty.
method Quantitative determination of project cost contingency reserves using Monte Carlo Simulation considering aleatoric, stochastic, and epistemic uncertainties.
result The proposed method provides more accurate contingency reserves that align with actual project risks.
Closed-form pricing method for multi-asset options.
problem Pricing multi-asset contingent claims in an incomplete market.
method Proving extremal martingale measures and constructing algorithms for bounds and hedging.
result Closed-form formulas for no-arbitrage price intervals and hedging strategies.
What is the role of real-time control and learning in the formation of social conventions? To answer this question, we propose a computational model that matches human behavioral data in a social decision-making game that was analyzed both in discrete-time and continuous-time setups. Furthermore, unlike previous approa…
We price a contingent claim liability using the utility indifference argument. We consider an agent with exponential utility, who invests in a stock and a money market account with the goal of maximizing the utility of his investment at the final time T in the presence of positive proportional transaction cost in two c…
Framework transfers limited steering angle data across multiple weather conditions.
problem Limited labeled data for diverse weather conditions in sensorimotor control.
method Teacher-student learning paradigm with image-to-image translation network.
result Framework generalizes well across multiple weather conditions using limited labels.
The paper explores how contingency-awareness improves exploration in reinforcement learning.
problem Improving exploration in reinforcement learning environments with sparse rewards.
method Developed an attentive dynamics model (ADM) to discover controllable elements of observations and used it for state representation in exploration.
result Combining actor-critic algorithms with count-based exploration using the ADM representation achieved impressive results on Atari games.
The paper defines and implements risk-indifference pricing for American-style contingent claims.
problem Pricing American-style contingent claims under uncertainty.
method Indifference pricing using convex risk measures and stochastic volatility models, with numerical solutions via deep learning.
result Characterization of indifference prices via Backward Stochastic Differential Equations (BSDEs).
In this paper, we study the pricing of contingent claims under G-expectation. In order to accomodate volatility uncertainty, the price of the risky security is supposed to governed by a general linear stochastic differential equation (SDE) driven by G-Brownian motion. Utilizing the recently developed results of Backwar…
We develop a pricing model for Sovereign Contingent Convertible bonds (S-CoCo) with payment standstills triggered by a sovereign's Credit Default Swap (CDS) spread. We model CDS spread regime switching, which is prevalent during crises, as a hidden Markov process, coupled with a mean-reverting stochastic process of spr…
We propose a new definition for tameness within the model of security prices as Itô processes that is risk-aware. We give a new definition for arbitrage and characterize it. We then prove a theorem that can be seen as an extension of the second fundamental theorem of asset pricing, and a theorem for valuation of contin…
Researchers develop a pricing method for contingent claims under partial information and short selling constraints.
problem Pricing contingent claims with partial information and short selling restrictions.
method Derive a dual problem using conjugate duality theory and conditions for strong duality.
result Characterization of contingent claim prices involving martingale and super-martingale conditions.
Model compresses event-like contexts using gated surprise signals.
problem Perceiving a dynamic world as organized events.
method Hierarchical, surprise-gated recurrent neural network architecture.
result Achieves best performance on multiple event processing tasks.
Improved method for encoding contingency tables reduces mutual information bias.
problem Mutual information bias in measuring label similarity.
method Improved method for encoding contingency tables to reduce information cost.
result Better bound on reduced mutual information in typical use cases.
Study upper hedging prices for contingent claims in models with various types of arbitrage.
problem Valuation of contingent claims in market models with different types of arbitrage.
method Analysis of market models with increasing profit, strong arbitrage, and arbitrage of the first kind.
result Option prices are reduced when increasing profit is present, and corporate stock price processes can be derived from issuance and repurchase plans.
This paper studies how modular agents can learn to control complex morphologies.
problem Contemporary sensorimotor learning starts with existing complex agents, but this paper explores learning from primitive, modular agents.
method A collection of primitive agents learns to dynamically self-assemble into composite bodies and coordinate their behavior to control these bodies.
result Dynamic and modular agents demonstrate better generalization to test-time changes in both environment and agent structure.
New PDEs model implied volatility without prior knowledge.
problem Modeling implied volatility without prior knowledge.
method Derived backward and forward nonlinear PDEs, discussed initial and boundary conditions, solved numerically.
result Solved PDEs for implied volatility of positive stock price contingent claims.
The paper extends portfolio theory to include contingent claim functions for option pricing.
problem Developing a method to price options using portfolio generating functions.
method Extending portfolio theory to include contingent claim functions and applying partial differential equations.
result A method to price options using portfolio generating functions and replicable contingent claim functions.
The target of this paper is to establish the bid-ask pricing frame work for the American contingent claims against risky assets with G-asset price systems (see \cite{Chen2013b}) on the financial market under Knight uncertainty. First, we prove G-Dooby-Meyer decomposition for G-supermartingale. Furthermore, we consider …
The paper examines how CoCo bonds can enhance financial stability in interconnected banking systems.
problem Enhancing financial stability in interconnected banking systems.
method Financial network model with contingent convertible (CoCo) debt obligations.
result Replacing unsecured interbank debt with CoCo debt decreases systemic risk and increases bank shareholder value.
We price weather-contingent options by use of Monte Carlo simulations. After calibrating the models to fit quoted prices, we analyze bid-ask spreads in terms of correlations across markets. Results are presented for a double-trigger Weather vs. Natural Gas call option.
New method for superhedging without assuming continuous claims.
problem Superhedging without assuming upper semicontinuous contingent claims.
method Established a generalized duality for model-free superhedging using Choquet's capacitability theorem.
result Generalized duality for superhedging given marginal distributions without continuity assumptions.
In this work we study the price-hedge issue for general defaultable contracts characterized by the presence of a contingent CSA of switching type. This is a contingent risk mitigation mechanism that allow the counterparties of a defaultable contract to switch from zero to full/perfect collateralization and switch back …
The paper extends ERP framework to non-monotonic payoffs and short selling bans.
problem Valuation of contingent claims with short selling bans under ERP framework.
method Unified framework for ERP pricing, extending to non-monotonic payoffs, and comparing with Black-Scholes.
result Equal-risk prices differ from Black-Scholes prices under short selling bans.
New pricing framework allocates costs of operating reserves and transmission.
problem Allocating costs of operating reserves and transmission efficiently.
method Causation-based framework using contingency-constrained scheduling models.
result More comprehensive and efficient cost-reflective market operations.
This article focuses on the mathematical problem of existence and uniqueness of BSDE with a random terminal time which is a general random variable but not a stopping time, as it has been usually the case in the previous literature of BSDE with random terminal time. The main motivation of this work is a financial or ac…
The paper prices a new life insurance policy for couples, considering various contingent benefits.
problem Valuation of life insurance policies for dependent coupled lives.
method Employed the QP-rule combining real-world and risk-neutral measures for pricing. result Explicit pricing expressions for the new insurance policy were derived and analyzed.
Paper introduces benchmark-neutral pricing for long-term contracts.
problem High prices of long-term contracts under risk-neutral pricing.
method Uses growth optimal portfolio as numeraire and new pricing measure.
result Identifies minimal possible prices for contingent claims.