Information Gathering Agents Research Articles

Informative Path Planning for Improving Classification Performance through Kinematic Decisions

As an information-gathering agent, unmanned system inevitably relies on its sensing abilities to perceive the world. Such a gathered information is vital for making further significant decisions, thus often operating under the assumption that maximizing the amount of information would result in minimizing the likelihood of committing erroneous decisions. In this work, we question this assumption by carefully examining the relationship between information and the probability of making erroneous decisions, then investigate the implication of the relationship in a Unmanned Air Vehicle (UAV) path planning problem. First, we conduct a functional analysis of two performance metrics (i.e., mutual information and the probability of misclassification) with respect to the sensing abilities. The analysis suggests an effective region of sensor space that can improve the classification performance when multiple measurements are to be taken sequentially. Based on the results of the analysis, we establish sensing strategies to a UAV path planning problem where the sensor performance depends on the relative position (i.e., range and azimuth) of the UAV with respect to the object of interest. Specifically, we use two sliding mode controllers, each of which accounts for a particular sensing strategy, with a hybrid-system switching scheme. We validate our approach with numerical simulation results.

Near-optimal continuous patrolling with teams of mobile information gathering agents

Autonomous unmanned vehicles equipped with sensors are rapidly becoming the de facto means of achieving situational awareness — the ability to make sense of, and predict what is happening in an environment. Particularly in environments that are subject to continuous change, the use of such teams to maintain accurate and up-to-date situational awareness is a challenging problem. To perform well, the vehicles need to patrol their environment continuously and in a coordinated manner.To address this challenge, we develop a near-optimal multi-agent algorithm for continuously patrolling such environments. We first define a general class of multi-agent information gathering problems in which vehicles are represented by information gathering agents — autonomous entities that direct their activity towards collecting information with the aim of providing accurate and up-to-date situational awareness. These agents move on a graph, while taking measurements with the aim of maximising the cumulative discounted observation value over time. Here, observation value is an abstract measure of reward, which encodes the properties of the agentsʼ sensors, and the spatial and temporal properties of the measured phenomena. Concrete instantiations of this class of problems include monitoring environmental phenomena (temperature, pressure, etc.), disaster response, and patrolling environments to prevent intrusions from (non-strategic) attackers.In more detail, we derive a single-agent divide and conquer algorithm to compute a continuous patrol (an infinitely long path in the graph) that yields a near-optimal amount of observation value. This algorithm recursively decomposes the graph, until high-quality paths in the resulting components can be computed outright by a greedy algorithm. It then constructs a patrol by concatenating these paths using dynamic programming. For multiple agents, the algorithm sequentially computes patrols for each agent in a greedy fashion, in order to maximise its marginal contribution to the team. Moreover, to achieve robustness, we develop algorithms for repairing patrols when one or more agents fail or the graph changes.For both the single- and the multi-agent case, we give theoretical guarantees (lower bounds on the solution quality and an upper bound on the computational complexity in the size of the graph and the number agents) on the performance of the algorithms. We benchmark the single- and multi-agent algorithm against the state of the art and demonstrate that it typically performs 35% and 33% better in terms of average and minimum solution quality respectively.

Informed principal and information gathering agent

We study a principal–agent model in which the principal has a production technology. The efficiency parameter of the principal’s technology is not known to the agent. Alternatively, the principal can make the agent use a technology from a different channel. By gathering information at a cost, the agent can be informed privately of the efficiency of the technology that he may acquire from another source. We find that the principal requires the agent to adopt the principal’s technology more (less) often when the cost of gathering information on the other technology is small (large). Also, with two states of nature, the outcome is first-best when the information gathering cost is intermediate.

Intelligent systems for tourism

The authors discuss travel recommender systems, adaptive context aware mobility support for tourists, tourism information systems, information delivery and travel planning information gathering agents.

Domain-dependent information gathering agent

Abstract A universal agent should be capable of gathering information from arbitrary heterogeneous sites and offer intelligent information services on its own based on information so gathered. We present a domain-dependent agent for information gathering. It can visit an arbitrary domain-related site by observing a user perform the first query. By understanding key concepts of the first query, the agent performs subsequent queries autonomously. When a user asks the agent about a particular item, the agent gathers relevant information from various sites. The major advantage of the agent is a semi-automatic creation of a wrapper around a particular site with few human interventions. We have implemented two versions of such information gathering agents: ShinA (SHoppINg Assistant) for e-trading tasks, and EMA (EMployment Agent), which performs employment and job related functions over the Internet.

BIG: An agent for resource-bounded information gathering and decision making

The World Wide Web has become an invaluable information resource but the explosion of available information has made Web search a time consuming and complex process. The large number of information sources and their different levels of accessibility, reliability and associated costs present a complex information gathering control problem. This paper describes the rationale, architecture, and implementation of a next generation information gathering system—a system that integrates several areas of Artificial Intelligence research under a single umbrella. Our solution to the information explosion is an information gathering agent, BIG, that plans to gather information to support a decision process, reasons about the resource trade-offs of different possible gathering approaches, extracts information from both unstructured and structured documents, and uses the extracted information to refine its search and processing activities.

Patrolling the neighborhood beat: Residents and residential security : by Robert K. Yin, Mary E. Vogel, Jan M. Chaiken, and Deborah R. Both. The Rand Corporation (1700 Main Street, Santa Monica, California 90406), 1976, 3 vols.—basic volume, 154 pp., $7.00; Vol. I, 39 pp., $3.00; Vol. II, 321 pp., $10.00.

Autonomous unmanned vehicles equipped with sensors are rapidly becoming the de facto means of achieving situational awareness — the ability to make sense of, and predict what is happening in an environment. Particularly in environments that are subject to continuous change, the use of such teams to maintain accurate and up-to-date situational awareness is a challenging problem. To perform well, the vehicles need to patrol their environment continuously and in a coordinated manner.To address this challenge, we develop a near-optimal multi-agent algorithm for continuously patrolling such environments. We first define a general class of multi-agent information gathering problems in which vehicles are represented by information gathering agents — autonomous entities that direct their activity towards collecting information with the aim of providing accurate and up-to-date situational awareness. These agents move on a graph, while taking measurements with the aim of maximising the cumulative discounted observation value over time. Here, observation value is an abstract measure of reward, which encodes the properties of the agentsʼ sensors, and the spatial and temporal properties of the measured phenomena. Concrete instantiations of this class of problems include monitoring environmental phenomena (temperature, pressure, etc.), disaster response, and patrolling environments to prevent intrusions from (non-strategic) attackers.In more detail, we derive a single-agent divide and conquer algorithm to compute a continuous patrol (an infinitely long path in the graph) that yields a near-optimal amount of observation value. This algorithm recursively decomposes the graph, until high-quality paths in the resulting components can be computed outright by a greedy algorithm. It then constructs a patrol by concatenating these paths using dynamic programming. For multiple agents, the algorithm sequentially computes patrols for each agent in a greedy fashion, in order to maximise its marginal contribution to the team. Moreover, to achieve robustness, we develop algorithms for repairing patrols when one or more agents fail or the graph changes.For both the single- and the multi-agent case, we give theoretical guarantees (lower bounds on the solution quality and an upper bound on the computational complexity in the size of the graph and the number agents) on the performance of the algorithms. We benchmark the single- and multi-agent algorithm against the state of the art and demonstrate that it typically performs 35% and 33% better in terms of average and minimum solution quality respectively.