Eco Mobility Game eMission – kleinschalige evaluatie

Neural-Symbolic Cognitive Agents: Architecture, Theory and Application (Extended Abstract)

In real-world applications, the effective integration of learning and reasoning in a cognitive agent model is a diffcult task. However, such integration may lead to a better understanding, use and construction of more realistic multiagent models. Existing models are either oversimplified or require too much processing time, which is unsuitable...

A search engine for retrieval and inspection of events with 48 human actions in realistic videos

The contribution of this paper is a search engine that recognizes and describes 48 human actions in realistic videos. The core algorithms have been published recently, from the early visual processing (Bouma, 2012), discriminative recognition (Burghouts, 2012) and textual description (Hankmann, 2012) of 48 human actions. We summarize the key...

Recognition and localization of relevant human behavior in videos

Ground surveillance is normally performed by human assets, since it requires visual intelligence. However, especially for military operations, this can be dangerous and is very resource intensive. Therefore, unmanned autonomous visual-intelligence systems are desired. In this paper, we present an improved system that can recognize actions of a...

Dreaming Machines: On multimodal fusion and information retrieval using neural-symbolic cognitive agents

Deep Boltzmann Machines (DBM) have been used as a computational cognitive model in various AI-related research and applications, notably in computational vision and multimodal fusion. Being regarded as a biological plausible model of the human brain, the DBM is also becoming a popular instrument to investigate various cortical processes in...

ePartner architecture workshops 'The Results'

Automatic Human Action Recognition in a Scene from Visual Inputs

Surveillance is normally performed by humans, since it requires visual intelligence. However, it can be dangerous, especially for military operations. Therefore, unmanned visual-intelligence systems are desired. In this paper, we present a novel system that can recognize human actions. Central to the system is a break-down of high-level...

A Neural-Symbolic Cognitive Agent with a Mind’s Eye

The DARPA Mind’s Eye program seeks to develop in machines a capability that currently exists only in animals: visual intelligence. This paper describes a Neural-Symbolic Cognitive Agent that integrates neural learning, symbolic knowledge representation and temporal reasoning in a visual intelligent system that can reason about actions of...

Neural-symbolic cognitive agents: Architecture and theory

In real-world applications, the effective integration of learning and reasoning in a cognitive agent model is a difficult task. However, such integration may lead to a better understanding, use and construction of more realistic models. Unfortunately, existing models are either oversimplified or require much processing time, which is unsuitable...

Visual intelligence using neural-symbolic learning and reasoning

The DARPA Mind’s Eye program seeks to develop in machines a capability that currently exists only in animals: visual intelligence. This short paper describes the initial results of a Neural-Symbolic approach for action recognition and description to be demonstrated at the 7th international workshop on Neural-Symbolic Learning and Reasoning.

A neural-symbolic cognitive agent for online learning and reasoning

In real-world applications, the effective integration of learning and reasoning in a cognitive agent model is a difficult task. However, such integration may lead to a better understanding, use and con-struction of more realistic models. Unfortunately, existing models are either oversimplified or require much processing time, which is unsuitable...

An integrated neural-symbolic cognitive agent architecture for training and assessment in simulators

Training and assessment of complex tasks has always been a complex task in itself. Training simulators can be used for training and assessment of low-order skills. High-order skills (e.g. safe driving, leadership, tactical manoeuvring, etc.) are generally trained and assessed by human experts, due to its complex nature (i.e. many temporal...

Human Performance Assessment in Driving Simulators Phase 1: Theoretical Backgrounds

In theory, driving simulators are ideal for assessment of driving performance. Driving simulators are reliable and objective measurement instruments and seem to have a high validity with regard to the actual driving task. Furthermore, in a driving simulator, one can control the traffic situation that is presented to the driver. Thus, driving...

Automated performance assessment and adaptive training for training simulators with SimSCORM

Performance assessment in training simulators for learning complex tasks is a complex task in itself. It requires monitoring and interpreting the student’s behavior in the simulator using knowledge of the training task, the environment, and a lot of experience. Assessment in simulators is therefore generally done by human observers. To capture...

A neural-symbolic system for automated assessment in training simulators - A position paper

Performance assessment in training simulators is a complex task. It requires monitoring and interpreting the student’s behaviour in the simulator using knowledge of the training task, the environment and a lot of experience. Assessment in simulators is therefore generally done by human observers. To capture this process in an automated system is...