Queue-based agent communication architecture for intelligent human-like computer interfaces

Human-like computer interaction systems requires far more than just simple speech input/output. Such a system should communicate with the user verbally, using a conversational style language. It should be aware of its surroundings and use this context for any decisions it makes. As a synthetic character, it should have a computer generated human-like appearance. This, in turn, should be used to convey emotions, expressions and gestures. Finally, and perhaps most important of all, the system should interact with the user in real time, in a fluent and believable manner.

Robots can't really be intelligent can they?

The whole concept of just what is and what is not, intelligence is a vitally important one. As humans interact more with machines, so the similarities and differences between human and machine intelligence need to be looked at in a sensible, scientific way. This paper considers human and machine intelligence and links them closely to physical characteristics, as exhibited by robots. Potential interfaces between humans and machines are also considered, as is the state of the art in direct physical links between humans and machines.

Intelligent methods for load forecasting

Load forecasting is an important task in the management of a power utility. The most recent developments in forecasting involve the use of artificial intelligence techniques, which offer powerful modelling capabilities. This paper discusses these techniques and provides a review of their application to load forecasting.

Path planning of robots in noisy workspaces using learning automata

The problem of a manipulator operating in a noisy workspace and required to move from an initial fixed position P0 to a final position Pf is considered. However, Pf is corrupted by noise, giving rise to Pˆf, which may be obtained by sensors. The use of learning automata is proposed to tackle this problem. An automaton is placed at each joint of the manipulator which moves according to the action chosen by the automaton (forward, backward, stationary) at each instant. The simultaneous reward or penalty of the automata enables avoiding any inverse kinematics computations that would be necessary if the distance of each joint from the final position had to be calculated. Three variable-structure learning algorithms are used, i.e., the discretized linear reward-penalty (DLR-P, the linear reward-penalty (LR-P ) and a nonlinear scheme. Each algorithm is separately tested with two (forward, backward) and three forward, backward, stationary) actions.

A variable radius niching technique for speciation in genetic algorithms

In this paper, a continuation of a variable radius niche technique called Dynamic Niche Clustering developed by (Gan & Warwick, 1999) is presented. The technique employs a separate dynamic population of overlapping niches that coexists alongside the normal population. An empirical analysis of the updated methodology on a large group of standard optimisation test-bed functions is also given. The technique is shown to perform almost as well as standard fitness sharing with regards to stability and the accuracy of peak identification, but it outperforms standard fitness sharing with regards to time complexity. It is also shown that the technique is capable of forming niches of varying size depending on the characteristics of the underlying peak that the niche is populating.

Recent developments in intelligent control

Intelligent control, as a discipline, has certainly been one of main growth areas in the field of control systems over the last 5-10 years. Although the topic is relatively new in itself, a number of other research areas, some of them well established, have effectively been swallowed up under the overall intelligent control umbrella. This paper defines intelligent control and identifies the main sub-areas in which significant progress has been made and likely fruitful topics to pursue in the future.