A fundamental problem in artificial intelligence is that nobody really knows what intelligence is. The problem is especially acute when we need to consider artificial systems which are significantly different to humans. In this paper we approach this problem in the following way: We take a number of well known informal definitions of human intelligence that have been given by experts, and extract their essential features. These are then mathematically formalised to produce a general measure of intelligence for arbitrary machines. We believe that this measure formally captures the concept of machine intelligence in the broadest reasonable sense.

Dynamic task allocation is an essential requirement for multi-robot systems operating in unknown dynamic environments. It allows robots to change their behavior in response to environmental changes or actions of other robots in order to improve overall system performance. Emergent coordination algorithms for task allocation that use only local sensing and no direct communication between robots are attractive because they are robust and scalable. However, a lack of formal analysis tools makes emergent coordination algorithms difficult to design. In this paper we present a mathematical model of a general dynamic task allocation mechanism. Robots using this mechanism have to choose between two types of task, and the goal is to achieve a desired task division in the absence of explicit communication and global knowledge. Robots estimate the state of the environment from repeated local observations and decide which task to choose based on these observations. We model the robots and observations as stochastic processes and study the dynamics of the collective behavior. Specifically, we analyze the effect that the number of observations and the choice of the decision function have on the performance of the system. The mathematical models are validated in a multi-robot multi-foraging scenario. The model's predictions agree very closely with experimental results from sensor-based simulations.

Through the Internet and the World-Wide Web, a vast number of information sources has become available, which offer information on various subjects by different providers, often in heterogeneous formats. This calls for tools and methods for building an advanced information-processing infrastructure. One issue in this area is the selection of suitable information sources in query answering. In this paper, we present a knowledge-based approach to this problem, in the setting where one among a set of information sources (prototypically, data repositories) should be selected for evaluating a user query. We use extended logic programs (ELPs) to represent rich descriptions of the information sources, an underlying domain theory, and user queries in a formal query language (here, XML-QL, but other languages can be handled as well). Moreover, we use ELPs for declarative query analysis and generation of a query description. Central to our approach are declarative source-selection programs, for which we define syntax and semantics. Due to the structured nature of the considered data items, the semantics of such programs must carefully respect implicit context information in source-selection rules, and furthermore combine it with possible user preferences. A prototype implementation of our approach has been realized exploiting the DLV KR system and its plp front-end for prioritized ELPs. We describe a representative example involving specific movie databases, and report about experimental results.

We investigate knowledge exchange among commercial organi sations, the rationale behind it and its effects on the marke t. Knowledge exchange is known to be beneficial for industry, bu t in order to explain it, authors have used high level concept s like network effects, reputation and trust. We attempt to formal ise a plausible and elegant explanation of how and why compan ies adopt information exchange and why it benefits the market as a whole when this happens. This explanation is based on a multi - agent model that simulates a market of software providers. E ven though the model does not include any high-level concept s, information exchange naturally emerges during simulation s as a successful profitable behaviour. The conclusions reac hed by this agent-based analysis are twofold: (1) A straightforward se t of assumptions is enough to give rise to exchange in a softwa re market. This work was carried out when M. Chli and P . The growth of the Internet as a medium of knowledge exchange has stimulated a lot of scientific interest origina ting from various disciplines. The willingness of individua ls, organisations as well as commercial firms to share information via the Internet has been remarkable. In some sectors like scientific research, the communication of newly acquir ed knowledge and expertise in a field is considered vital for the ir advancement. On the other hand, in other sectors, the benefit s of such exchanges may not be obvious. For instance, it might even be considered damaging for pharmaceutical companies t o make public any innovations generated by their Research and Development (R&D) process. In spite of this view, exchange o f intellectual property in some industries occurs quite freq uently and in various different ways. These include the forming of strategic partnerships, the participation in open source s oftware projects and the publication of scientific papers by researc h labs that are part of commercial companies. W e study the knowledge exchange that occurs in the software industry. In particular, we focus on analysing the rationale behind this exchange as well as its effect on the industry. The complexity of software requirements is a char - acteristic that distinguishes the software market from oth ers.

Although the Internet AS-level topology has been extensively studied over the past few years, little is known about the details of the AS taxonomy. An AS "node" can represent a wide variety of organizations, e.g., large ISP, or small private business, university, with vastly different network characteristics, external connectivity patterns, network growth tendencies, and other properties that we can hardly neglect while working on veracious Internet representations in simulation environments. In this paper, we introduce a radically new approach based on machine learning techniques to map all the ASes in the Internet into a natural AS taxonomy. We successfully classify 95.3% of ASes with expected accuracy of 78.1%. We release to the community the AS-level topology dataset augmented with: 1) the AS taxonomy information and 2) the set of AS attributes we used to classify ASes. We believe that this dataset will serve as an invaluable addition to further understanding of the structure and evolution of the Internet.

Physical processes are computations only when we use them to externalize thought. Computation is the performance of one or more fixed processes within a contingent environment. We reformula te the Church-Turing thesis so that it applies to programs rather than to c omputability. When suitably formulated agent-based computing in an open, multi-scalar environment represents the current consensus view of how we interact with the world. But we don't know how to formulate multi-scalar environments. Keywords: agents, agent-based, agent-based computation, Church-Turing thesis, Church's thesis, computing, computation, envir onment ideas, interaction, interactive computation, models, multi-scalar envir onment, thought, thought tools, unconventional computation.

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.

A good organization model of multiple mobile robot should be able to improve the efficiency of the system, reduce the complication of robot interactions, and detract the difficulty of computation. From the sociology aspect of topology, structure and organization, this paper studies the multiple mobile robot organization formation and running mechanism in the dynamic, complicated and unknown environment. It presents and describes in detail a Hierarchical- Web Recursive Organization Model (HWROM) and forming algorithm. It defines the robot society leader; robotic team leader and individual robot as the same structure by the united framework and describes the organization model by the recursive structure. The model uses task-oriented and top-down method to dynamically build and maintain structures and organization. It uses market-based techniques to assign task, form teams and allocate resources in dynamic environment. The model holds several characteristics of self-organization, dynamic, conciseness, commonness and robustness.

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.

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.