3,649 research outputs found
COMBINED ARTIFICIAL INTELLIGENCE BEHAVIOUR SYSTEMS IN SERIOUS GAMING
This thesis proposes a novel methodology for creating Artificial Agents with semi-realistic behaviour, with such behaviour defined as overcoming common limitations of mainstream behaviour systems; rapidly switching between actions, ignoring “obvious” event priorities, etc. Behaviour in these Agents is not fully realistic as some limitations remain; Agents have a “perfect” knowledge about the surrounding environment, and an inability to transfer knowledge to other Agents (no communication).
The novel methodology is achieved by hybridising existing Artificial Intelligence (AI) behaviour systems. In most artificial agents (Agents) behaviour is created using a single behaviour system, whereas this work combines several systems in a novel way to overcome the limitations of each. A further proposal is the separation of behavioural concerns into behaviour systems that are best suited to their needs, as well as describing a biologically inspired memory system that further aids in the production of semi-realistic behaviour.
Current behaviour systems are often inherently limited, and in this work it is shown that by combining systems that are complementary to each other, these limitations can be overcome without the need for a workaround. This work examines in detail Belief Desire Intention systems, as well as Finite State Machines and explores how these methodologies can complement each other when combined appropriately. By combining these systems together a hybrid system is proposed that is both fast to react and simple to maintain by separating behaviours into fast-reaction (instinctual) and slow-reaction (behavioural) behaviours, and assigning these to the most appropriate system. Computational intelligence learning techniques such as Artificial Neural Networks have been intentionally avoided, as these techniques commonly present their data in a “black box” system, whereas this work aims to make knowledge explicitly available to the user.
A biologically inspired memory system has further been proposed in order to generate additional behaviours in Artificial Agents, such as behaviour related to forgetfulness. This work explores how humans can quickly recall information while still being able to store millions of pieces of information, and how this can be achieved in an artificial system
Lifelong Learning of Spatiotemporal Representations with Dual-Memory Recurrent Self-Organization
Artificial autonomous agents and robots interacting in complex environments
are required to continually acquire and fine-tune knowledge over sustained
periods of time. The ability to learn from continuous streams of information is
referred to as lifelong learning and represents a long-standing challenge for
neural network models due to catastrophic forgetting. Computational models of
lifelong learning typically alleviate catastrophic forgetting in experimental
scenarios with given datasets of static images and limited complexity, thereby
differing significantly from the conditions artificial agents are exposed to.
In more natural settings, sequential information may become progressively
available over time and access to previous experience may be restricted. In
this paper, we propose a dual-memory self-organizing architecture for lifelong
learning scenarios. The architecture comprises two growing recurrent networks
with the complementary tasks of learning object instances (episodic memory) and
categories (semantic memory). Both growing networks can expand in response to
novel sensory experience: the episodic memory learns fine-grained
spatiotemporal representations of object instances in an unsupervised fashion
while the semantic memory uses task-relevant signals to regulate structural
plasticity levels and develop more compact representations from episodic
experience. For the consolidation of knowledge in the absence of external
sensory input, the episodic memory periodically replays trajectories of neural
reactivations. We evaluate the proposed model on the CORe50 benchmark dataset
for continuous object recognition, showing that we significantly outperform
current methods of lifelong learning in three different incremental learning
scenario
Minimalistic vision-based cognitive SLAM
The interest in cognitive robotics is still increasing, a major goal being to create a system which can adapt
to dynamic environments and which can learn from its own experiences. We present a new cognitive SLAM
architecture, but one which is minimalistic in terms of sensors and memory. It employs only one camera with
pan and tilt control and three memories, without additional sensors nor any odometry. Short-term memory is
an egocentric map which holds information at close range at the actual robot position. Long-term memory is
used for mapping the environment and registration of encountered objects. Object memory holds features of
learned objects which are used as navigation landmarks and task targets. Saliency maps are used to sequentially
focus important areas for object and obstacle detection, but also for selecting directions of movements.
Reinforcement learning is used to consolidate or enfeeble environmental information in long-term memory.
The system is able to achieve complex tasks by executing sequences of visuomotor actions, decisions being
taken by goal-detection and goal-completion tasks. Experimental results show that the system is capable of
executing tasks like localizing specific objects while building a map, after which it manages to return to the
start position even when new obstacles have appeared
Biologically Inspired Vision for Indoor Robot Navigation
Ultrasonic, infrared, laser and other sensors are being applied
in robotics. Although combinations of these have allowed robots to navigate,
they are only suited for specific scenarios, depending on their limitations.
Recent advances in computer vision are turning cameras into useful
low-cost sensors that can operate in most types of environments. Cameras
enable robots to detect obstacles, recognize objects, obtain visual
odometry, detect and recognize people and gestures, among other possibilities.
In this paper we present a completely biologically inspired vision
system for robot navigation. It comprises stereo vision for obstacle detection,
and object recognition for landmark-based navigation. We employ
a novel keypoint descriptor which codes responses of cortical complex
cells. We also present a biologically inspired saliency component, based
on disparity and colour
What is Computational Intelligence and where is it going?
What is Computational Intelligence (CI) and what are its relations with Artificial Intelligence (AI)? A brief survey of the scope of CI journals and books with ``computational intelligence'' in their title shows that at present it is an umbrella for three core technologies (neural, fuzzy and evolutionary), their applications, and selected fashionable pattern recognition methods. At present CI has no comprehensive foundations and is more a bag of tricks than a solid branch of science. The change of focus from methods to challenging problems is advocated, with CI defined as a part of computer and engineering sciences devoted to solution of non-algoritmizable problems. In this view AI is a part of CI focused on problems related to higher cognitive functions, while the rest of the CI community works on problems related to perception and control, or lower cognitive functions. Grand challenges on both sides of this spectrum are addressed
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