Ant colony optimization (ACO) algorithms are computational problem-solving methods that are inspired by the complex behaviors of ant colonies; specifically, the ways in which ants interact with each other and their environment to optimize the overall performance of the ant colony. Our eventual goal is to develop and experiment with ACO methods that can more effectively adapt to dynamically changing environments and problems. We describe biological ant systems and the dynamics of their environments and behaviors. We then introduce a family of dynamic ACO algorithms that can handle dynamic modifications of their inputs. We report empirical results, showing that dynamic ACO algorithms can effectively adapt to time-varying environments.

In the context of cognitive architectures, memory is typically considered as a passive storage device with the sole purpose of maintaining and retrieving information relevant to ongoing cognitive processing. If memory is instead considered to be a fundamentally active aspect of cognition, as increasingly suggested by empirically-derived neurophysiological theory, this passive role must be reinterpreted. In this perspective, memory is the distributed substrate of cognition, forming the foundation for cross-modal priming, and hence soft cross-modal coordination. This paper seeks to describe what a cognitive architecture based on this perspective must involve, and initiates an exploration into how human-level cognitive competencies (namely episodic memory, word label conjunction learning, and social behaviour) can be accounted for in such a low-level framework. This proposal of a memory-centred cognitive architecture presents new insights into the nature of cognition, with benefits for computational implementations such as generality and robustness that have only begun to be exploited.

An important form of learning involves acquiring skills that let an agent achieve its goals. While there has been considerable work on learning in planning, most approaches have been sensitive to the representation of domain context, which hurts their generality. A learning mechanism that constructs skills effectively across different representations would suggest more robust behavior. In this paper, we present a novel approach to learning hierarchical task networks that acquires conceptual predicates as learning proceeds, making it less dependent on carefully crafted background knowledge. The representation acquisition procedure expands the system's knowledge about the world, and leads to more rapid learning. We show the effectiveness of the approach by comparing it with one that doesnot change domain representation.

In this paper, we propose a simulation platform for the performance testing of robots and human operations. Robots have been used in disaster scenarios, where the environment is unstable. Human operators may have no prior experience in dealing with such dynamically changing environments, which may also be unstable for robotic tasks. To develop rescue robots, disaster situation emulation and human-in-loop test platform are required in addition to robot simulators. The proposed platform is used to design, develop robots and to conduct drills for robot operations, and to carry out experiments. And the results of experiments are presented.

We present a bi-threshold model of complex contagion in networks. In this model a node in a network can be in one of two states at any time step, and changes state if enough of its neighbors are in the opposite state, as determined by “up-threshold” and “down-threshold” parameters. This dynamical process models several types of social contagion processes, such as public health concerns and the spread of games on online networks. Motivated by recent literature calling for the investigation of peer pressure to reduce obesity, which can be viewed as a control problem of population dynamics, we focus on the computational complexity of finding critical sets of nodes, which are nodes that we choose to freeze in state 0 (a desirable state) in order to inhibit the spread of an undesirable state 1 in the network. We define a minimum-cost critical set problem and show that it is NP-complete for bi-threshold systems. We show that several versions of the problem can be approximated to within a factor of O(log n), where n is the number of nodes in the network. Using the ideas behind these approximations, we devise a heuristic, called the Maximum Contributor Heuristic (MCH), which can be used even when the diffusion model is probabilistic. We perform simulations with well-known networks from the literature and show that MCH outperforms the High Degree Heuristic by several orders of magnitude.

The new scenarios of contemporary adaptive robotics seem to suggest a transformation of the traditional methods. In the search for new approaches to the control of adaptive autonomous systems, the mind becomes a fundamental source of inspiration. In this paper we anticipate, through the use of simulation, the cognitive and behavioral properties that emerge from a recent prototype robotic platform, EcoBot, a family of bio-mechatronic symbionts provided with an `artificial metabolism', that has been under physical development during recent years. Its energy reliance on a biological component and the consequent limitation of its supplied energy determine a special kind of dynamic coupling between the robot and its environment. Rather than just an obstacle, energetic constraints become the opportunity for the development of a rich set of behavioral and cognitive properties.

The concept of common ground — the mutual understanding of context and conventions — is central to philosophical accounts of mendacity; its use is to determine the meaning of linguistic expressions and the significance of physical acts, and to distinguish certain statements as conveying a conventional promise, warranty, or expectation of sincerity. Lying necessarily involves an abuse of common ground, namely the willful violation of conventions regulating sincerity. The ‘lying machine’ is an AI system that purposely abuses common ground as an effective means for practicing mendacity and lesser deceptions. The machine's method is to conceive and articulate sophisms — perversions of normative reason and communication — crafted to subvert its audience's beliefs. Elements of this paper (i) explain the described use of common ground in philosophical accounts of mendacity, (ii) motivate arguments and illusions as stratagem for deception, (iii) encapsulate the lying machine's design and operation, and (iv) summarize human-subject experiments that confirm the lying machine's arguments are, in fact, deceptive.

Inference techniques play a central role in many cognitive systems. They transform low-level observations of the environment into high-level, actionable knowledge which then gets used by mechanisms that drive action, problem-solving, and learning. This paper presents an initial effort at combining results from AI and psychology into a pragmatic and scalable computational reasoning system. Our approach combines a numeric notion of plausibility with first-order logic to produce an incremental inference engine that is guided by heuristics derived from the psychological literature. We illustrate core ideas with detailed examples and discuss the advantages of the approach with respect to cognitive systems.

This article proposes a suite of design decisions for the overall design of an Artificial Intelligence, i.e., a software system that exhibits intelligence in the spirit of the early days of A.I. research. The key aspects of the proposal are: (1) The identification of the A.I. system as a software individual that has the properties of integrity and persistence; (2) The construction of a software platform that integrates aspects of incremental programming languages and systems as well as of operating systems, with aspects that are intrinsic to knowledge-based artificial intelligence; (3) The use of a representation language that builds on essential aspects of S-expressions, Lisp, logic and extended set theory, but which is used both as a vehicle for software and as a publication language e.g. in lecture notes; (4) The identification of actions and aggregates of actions as first-class citizens in the representation language and as an important type of data object in the software system. The article also describes the Leonardo software platform, its representation language, its educational resources and its knowledgebase library which is one implementation of these proposed design decisions. Finally it makes a proposal concerning the research paradigm for this research area.

This paper explores the application of computer simulation techniques to the fields of literary studies and narratology by developing a model for plot structure and characterization. Using a corpus of 19th Century British novels as a case study, the author begins with a descriptive quantitative analysis of character names, developing a set of stylized facts about the way narratives allocate attention to their characters. The author shows that narrative attention in many novels appears to follow a “long tail” distribution.The author then constructs an explanatory model in NetLogo, demonstrating that basic assumptions about plot structure are sufficient to generate output consistent with the real novels in the corpus.