Narrow Results

In this paper, we address the deterministic rendezvous in graphs where k mobile agents, disseminated at different times and different nodes, have to meet in finite time at the same node. The mobile agents are autonomous, oblivious, labeled, and move asynchronously. Moreover, we consider an undirected anonymous connected graph. For this problem, we exhibit some asymptotical time and space lower bounds as well as some necessary conditions. We also propose an algorithm that is asymptotically optimal in both space and round complexities.

In Chinese document image processing, text and/or graphical block detection serves as an essential step in document layout analysis that in turn permits the effective reasoning about the logical relationships among various text paragraphs and graphical entities for the purpose of document understanding. This paper presents a novel computational paradigm for extracting text/graphic blocks from Chinese document images, which is based on a notion of distributed autonomous agents. The primary features of the agents lie in that they are (1) adaptive to the locality of given images and hence efficient in locating the homogeneous image blocks, (2) reliable in performing image processing as the computation proceeds simultaneously from different image locations, (3) less sensitive to the noise in the given images as the computation disperses gracefully when it is moving away from the homogeneous blocks, and (4) easy to represent in their behaviors and evolvable in their performance. The paper, first of all, describes the formalisms as well as the behavioral characteristics of the agents, which is followed by a demonstration of the agents in detecting document blocks from some real-life images.

Exploration is a central issue for autonomous agents which must carry out navigation tasks in environments of which a description is not known a priori. In our approach the environment is described, from a symbolic point of view, by means of a graph; clustering techniques allow for further levels of abstraction to be defined, leading to a multi-layered representation. In this work we propose an unsupervised exploration algorithm in which several agents cooperate to acquire knowledge of the environment at the different abstraction levels. All agents are equal and pursue the same local exploration strategy; nevertheless, the existence of multiple levels of abstraction in the environment representation allows for the agents' behavior to differ. Agents carry out exploration at different abstraction levels, aimed at reproducing an ideal exploration profile; each agent dynamically selects its exploration level, based on the current demand. Inter-agent communication allows for the agents to share their knowledge and to record acquaintances of the other agents. A communication protocol for organizing teams of agents is provided.

A new equation system (based on synchronization of coupled chaotic oscillators) is proposed for bringing autonomous mobile agents into formation. Chaotic itinerancy of the oscillators is used to generate the re-formation. Sannomiya's fish simulator is applied to test the system behavior and the re-formation performance was verified using trajectory and quantitative measures.

This paper presents the details of a mass evacuation simulator with complex autonomous agents on a high resolution model of environment along with demonstrative applications that highlight its usefulness, need and uniqueness. Most of existing mass evacuation simulators are based on simplified models, and the use of complex models is limited to small scale simulations. This simulator makes use of high performance computing to introduce a complex agent system to simulate evacuations in hundreds of square kilometer size domains. The framework of the developed multi-agent system and some of the agents’ constituent functions for interacting with high resolution grid are briefly explained. Interactions are validated using field observations. Two sets of applications are presented to demonstrate the systems use for simulating mixed mode evacuation and evacuation in dynamically changing environment.

Cognitive theories of consciousness should provide effective frameworks to implement machine consciousness. The Global Workspace Theory is a leading theory of consciousness which postulates that the primary function of consciousness is a global broadcast that facilitates recruitment of internal resources to deal with the current situation as well as modulate several types of learning. In this paper, we look at architectures for machine consciousness that have the Global Workspace Theory as their basis and discuss the requirements in such architectures to bring about both functional and phenomenal aspects of consciousness in machines.

This paper considers several aspects of the relationship between size, structure, speed of propagation and the number of autonomous cognitive agents in a neural network. While memory and function generation capacities of neural networks with scale-invariant structure have been investigated extensively, the number of autonomous agents, associated with the simultaneous processing of the independent components of the data, has not received prior attention. We propose the emergence of the dichotomy of causal and noncausal regions created by the speed of propagation, in which the autonomous cognitive agents are not bound in a causal relationship with other agents. Arguments are presented for why the count of autonomous agents is best estimated with respect to the dimensionality of the underlying space. The number of autonomous agents obtained for the human brain equals 25, and it is significant that the number in the sub-system modules also turns out to be close to the same value. This number equals the number of enumerative categories of reality in at least one philosophical tradition, and this coincidence may be attributed to the capacity of consciousness to reflect on its own structure. It is possible that near equality across layers provides a special uniqueness to the human brain. We propose that the findings of this study will be useful in the design of neural-network-based AI systems that are designed to emulate human cognitive capacity.

Software and knowledge engineers continually strive to develop tools and techniques to manage the complexity that is inherent in the systems they have to build. In this article, we argue that intelligent agents and agent-based systems offer novel opportunities for developing effective tools and techniques. Following a discussion on the classic subject of what makes software complex, we introduce intelligent agents as software structures capable of making “rational decisions”. Such rational decision-makers are well-suited to the construction of certain types of software, which mainstream software engineering has had little success with. We then go on to examine a number of prototype techniques proposed for engineering agent systems, including formal specification and verification methods for agent systems, and techniques for implementing agent specifications.

In Chinese document image processing, text and/or graphical block detection serves as an essential step in document layout analysis that in turn permits the effective reasoning about the logical relationships among various text paragraphs and graphical entities for the purpose of document understanding. This paper presents a novel computational paradigm for extracting text/graphic blocks from Chinese document images, which is based on a notion of distributed autonomous agents. The primary features of the agents lie in that they are (1) adaptive to the locality of given images and hence efficient in locating the homogeneous image blocks, (2) reliable in performing image processing as the computation proceeds simultaneously from different image locations, (3) less sensitive to the noise in the given images as the computation disperses gracefully when it is moving away from the homogeneous blocks, and (4) easy to represent in their behaviors and evolvable in their performance. The paper, first of all, describes the formalisms as well as the behavioral characteristics of the agents, which is followed by a demonstration of the agents in detecting document blocks from some real-life images.