A model of an organism as an au tonomous intelligent system has been proposed. This model was used to analyz e learning of an organism in various environmental conditions. Processes of learning were divided into two types: strong and weak processes taking place in the absence an d the presence of aprioristic information about an object respectively. Weak lear ning is synonymous to adaptation when aprioristic programs already available in a system (an organism) are started. It was shown that strong learning is impossible fo r both an organism and any autonomous intelligent system. It was shown also that the knowledge base of an organism cannot be updated. Therefore, all behavior programs of an organism are congenital. A model of a conditioned reflex as a series of consecutive measurements of environmental parameters has been advanced. Repeated measurements are necessary in this case to reduce the error during decision making.

Awareness has been shown to be a useful addition to standard epistemic logic for many applications. However, standard propositional logics for knowledge and awareness cannot express the fact that an agent knows that there are facts of which he is unaware without there being an explicit fact that the agent knows he is unaware of. We propose a logic for reasoning about knowledge of unawareness, by extending Fagin and Halpern's \emph{Logic of General Awareness}. The logic allows quantification over variables, so that there is a formula in the language that can express the fact that ``an agent explicitly knows that there exists a fact of which he is unaware''. Moreover, that formula can be true without the agent explicitly knowing that he is unaware of any particular formula. We provide a sound and complete axiomatization of the logic, using standard axioms from the literature to capture the quantification operator. Finally, we show that the validity problem for the logic is recursively enumerable, but not decidable.

In this paper after short review of the CSIEC project initialized by us in 2003 we present the continuing development and improvement of the CSIEC project in details, including the design of five new Microsoft agent characters representing different virtual chatting partners and the limitation of simulated dialogs in specific practical scenarios like graduate job application interview, then briefly analyze the actual conditions and features of its application field: web-based Englis h education in China. Finally we introduce our effort s to adapt this system to the requirements of English te aching and learning in China and point out the work next to do.

Instantaneously Trained Neural Networks Abhilash Ponnath Abstract: This paper presents a review of instantaneously trained neural networks (ITNNs). These networks trade learning time for size and, in the basic model, a new hidden node is created for each training sample. Various versions of the corner-classification family of ITNNs, which have f ound applications in artificial intelligence (AI), are described. Implementation issues are also considered. 1 Introduction The human brain, the most complex known living structure in the universe, has the nerve cell or neuron as its fundamental unit. The number of neurons and connections between the neurons is enormous; this ensemble enables the brain to surpass the computational capacity of supercomputers in existence today. Artificial neural networks (ANNs) are models of the brain, which implement the mapping, ƒ: X Y such that the task is completed in a "certain" sense.

This paper presents a new concept of a mobile robot speed control by using two degree of freedom gear transmission. The developed intelligent speed controller utilizes a gear box which comprises of epicyclic gear train with two inputs, one coupled with the engine shaft and another with the shaft of a variable speed dc motor. The net output speed is a combination of the two input speeds and is governed by the transmission ratio of the planetary gear train. This new approach eliminates the use of a torque converter which is otherwise an indispensable part of all available automatic transmissions, thereby reducing the power loss that occurs in the box during the fluid coupling. By gradually varying the speed of the dc motor a stepless transmission has been achieved. The other advantages of the developed controller are pulling over and reversing the vehicle, implemented by intelligent mixing of the dc motor and engine speeds. This approach eliminates traditional braking system in entire vehicle design. The use of two power sources, IC engine and battery driven DC motor, utilizes the modern idea of hybrid vehicles. The new mobile robot speed controller is capable of driving the vehicle even in extreme case of IC engine failure, for example, due to gas depletion.

There is a belief that complexity and chaos are essential for adaptability. But life deals with complexity every moment, without the chaos that engineers fear so, by invoking goal-directed behaviour. Goals can be programmed. That is why living organisms give us hope to achieve adaptability in robots. In this paper a method for the description of a goal-directed, or programmed, behaviour, interacting with uncertainty of environment, is described. We suggest reducing the structural (goals, intentions) and stochastic components (probability to realise the goal) of individual behaviour to random variables with nominal values to apply probabilistic approach. This allowed us to use a Normalized Entropy Index to detect the system state by estimating the contribution of each agent to the group behaviour. The number of possible group states is 27. We argue that adaptation has a limited number of possible paths between these 27 states. Paths and states can be programmed so that after adjustment to any particular case of task and conditions, adaptability will never involve chaos. We suggest the application of the model to operation of robots or other devices in remote and/or dangerous places.

At present, the research on robot team cooperation is still in qualitative analysis phase and lacks the description model that can quantitatively describe the dynamical evolution of team cooperative relationships with constantly changeable task demand in Multi-robot field. First this paper whole and static describes organization model HWROM of robot team, then uses Markov course and Bayesian theorem for reference, dynamical describes the team cooperative relationships building. Finally from cooperative entity layer, ability layer and relative layer we research team formation and cooperative mechanism, and discuss how to optimize relative action sets during the evolution. The dynamic evolution model of robot team and cooperative relationships between robot teams proposed and described in this paper can not only generalize the robot team as a whole, but also depict the dynamic evolving process quantitatively. Users can also make the prediction of the cooperative relationship and the action of the robot team encountering new demands based on this model. Journal web page & a lot of robotic related papers www.ars-journal.com

Its capabilities cover a wide range of possible styles of algorithms. It is al so a standard environment for testing different techniques of making standard software agents with distributed architecture[10]. Rescue Simulation System also prov ides a standard framework for testing proposed algorithms and mathematical models of disaster events[8]. Designing an autonomous agent set like the one that is required for RoboCup Rescue Simulation is a little bit more of a challenge. Planning effective collaboration for a Multi-Agent team in disastrous environments still remains a challenging area in AI. Efforts of Multi-Agent researchers have provided somewhat of a standard in modeling and designing software. A lot of effort has gone into reaching coordination between different agents and making autonomous decisions that work toward the team goal[9]. But practical results in complicated domains such as RoboCup Rescue Simulation indicate that heuristic criteria still remain as a major part of a successful system[11]. This may signal lack of satisfactory models for these complicated situations.

These mechanisms are built using lighter, cheaper materials, which improve the payload to arm weight ratio, thus resulting in an increase of the speed with lower energy consumption. Moreover these lightweight arms are more safely operated due to the reduced inertia and compliant structure, which is very co nvenient for delicate assembly tasks and interaction with fragile objects, including human beings. However, the dynamic analysis and control of flexible-link manipulators is much more complex than the analysis and control of the equivalent rigid manipulators. From the modelling standpoint, the challenges are associated with the fact th at the non-linear rigid body motions are now strongly coupled with the distributed effects of the flexibility along the mechanical structure. This coupling varies with the system configuration and the load inertia.

This article considers evidence from physical and biological sciences to show machines are deficient compared to biological systems at incorporating intelligence. Machines fall short on two counts: firstly, unlike brains, machines do not self-organize in a recursive manner; secondly, machines are based on classical logic, whereas Nature's intelligence may depend on quantum mechanics.