Lexical constraints on the input of speech and on-line handwriting systems improve the performance of such systems. A significant gain in speed can be achieved by integrating in a digraph structure the different Hidden Markov Models (HMM) corresponding to the words of the relevant lexicon. This integration avoids redundant computations by sharing intermediate results between HMM's corresponding to different words of the lexicon. In this paper, we introduce a token passing method to perform simultaneously the computation of the a posteriori probabilities of all the words of the lexicon. The coding scheme that we introduce for the tokens is optimal in the information theory sense. The tokens use the minimum possible number of bits. Overall, we optimize simultaneously the execution speed and the memory requirement of the recognition systems.

The problem of designing optimal trajectory for redundant manipulators has attracted many researchers for the last three decades. One of the main reasons is the use of kinematically redundant robots is expected to increase in the future due to their increased flexibility. Some of the extra capabilities include the ability to avoid internal singularities or exte rnal obstacles over their entire workspace (Parket et al.,1989). Also, the inverse kinematics problem is underdetermined and admits an infinite number of distinct feasible solutions, meaning that a given end-effector pos es can be realized by an infinite number of distinct manipulator configurations (McAvoy, et al, 2000). In order to overcome the shortcomings inherent in non-redundant robots, redundant robots have been utilized in industrial applications to increase fl exibility and dexterity around a restricted task space in pres ence of obstacle.

This is a presentation of a new system for invariant recognition of 2D objects with overlapping classes, that can not be effectively recognized with the traditional methods. The translation, scale and partial rotation invariant contour object description is transformed in a DCT spectrum space. The obtained frequency spectrums are decomposed into frequency bands in order to feed different BPG neural nets (NNs). The NNs are structured in three stages - filtering and full rotation invariance; partial recognition; general classification. The designed multi-stage BPG Neural Structure shows very good accuracy and flexibility when tested with 2D objects used in the discontinuous production. The reached speed and the opportunuty for an easy restructuring and reprogramming of the system makes it suitable for application in different applied systems for real time work.

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.

This paper is a summary of the recently accomplished research work on flexible gripping systems. The goal is to develop a gripper which can be used for a great amount of geometrically variant workpieces. The economic aspect is of particular importance during the whole development. The high flexibility of the gripper is obtained by three parallel used principles. These are human and computer based analysis of the gripping object as well as mechanical adaptation of the gripper to the object with the help of servo motors. The focus is on the gripping of free-form surfaces with suction cup.

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.

This workshop brings together practioners and researchers who are involved in the everyday aspects of logical systems based on higher-order logic. We hope to create a friendly and highly interactive setting for discussions around the following four topics. Implementation and development of proof assistants based on any notion of impredicativity, automated theorem proving tools for higher-order logic reasoning systems, logical framework technology for the representation of proofs in higher-order logic, formal digital libraries for storing, maintaining and querying databases of proofs. We envision attendees that are interested in fostering the development and visibility of reasoning systems for higher-order logics. We are particularly interested in a discusssion on the development of a higher-order version of the TPTP and in comparisons of the practical strengths of automated higher-order reasoning systems. Additionally, the workshop includes system demonstrations. ESHOL is the successor of the ESCAR and ESFOR workshops held at CADE 2005 and IJCAR 2004.

In this paper, a mathematical schema theory is developed. This theory has three roots: brain theory schemas, grid automata, and block-shemas. In Section 2 of this paper, elements of the theory of grid automata necessary for the mathematical schema theory are presented. In Section 3, elements of brain theory necessary for the mathematical schema theory are presented. In Section 4, other types of schemas are considered. In Section 5, the mathematical schema theory is developed. The achieved level of schema representation allows one to model by mathematical tools virtually any type of schemas considered before, including schemas in neurophisiology, psychology, computer science, Internet technology, databases, logic, and mathematics.

Researchers in the field of biocomputing have, for many years, successfully "harvested and exploited" the natural world for inspiration in developing systems that are robust, adaptable and capable of generating novel and even "creative" solutions to human-defined problems. However, in this position paper we argue that the time has now come for a reassessment of how we exploit biology to generate new computational systems. Previous solutions (the "first generation" of biocomputing techniques), whilst reasonably effective, are crude analogues of actual biological systems. We believe that a new, inherently inter-disciplinary approach is needed for the development of the emerging "second generation" of bio-inspired methods. This new modus operandi will require much closer interaction between the engineering and life sciences communities, as well as a bidirectional flow of concepts, applications and expertise. We support our argument by examining, in this new light, three existing areas of biocomputing (genetic programming, artificial immune systems and evolvable hardware), as well as an emerging area (natural genetic engineering) which may provide useful pointers as to the way forward.

The main theme of this workshop (Dagstuhl seminar 04351) is `Spatial Representation: Continuous vs. Discrete'. Spatial representation has two contrasting but interacting aspects (i) representation of spaces' and (ii) representation by spaces. In this paper, we will examine two aspects that are common to both interpretations of the theme, namely nerve constructions and refinement. Representations change, data changes, spaces change. We will examine the possibility of a `differential geometry' of spatial representations of both types, and in the sequel give an algebra of differential forms that has the potential to handle the dynamical aspect of such a geometry. We will discuss briefly a conjectured class of spaces, generalising the Cantor set which would seem ideal as a test-bed for the set of tools we are developing.