Agent-Based Team Aiding in a Time Critical Task

In this paper we evaluate the effectiveness of agent-based aiding in support of a time-critical team-planning task for teams of both humans and heterogeneous software agents. The team task consists of human subjects playing the role of military commanders and cooperatively planning to move their respective units to a common rendezvous point, given time and resource constraints. The objective of the experiment was to compare the effectiveness of agent-based aiding for individual and team tasks as opposed to the baseline condition of manual route planning. There were two experimental conditions: the Aided condition, where a Route Planning Agent (RPA) finds a least cost plan between the start and rendezvous points for a given composition of force units; and the Baseline condition, where the commanders determine initial routes manually, and receive basic feedback about the route. We demonstrate that the Aided condition provides significantly better assistance for individual route planning and team-based re-planning

Implications of Including the Developer in the IS Delegation Framework

The capabilities of artificial intelligence (AI) systems bring about new leaps in organizational change. While several work processes are now digital, and are increasingly getting automated, organizational processes thrive from augmenting AI-based tasks and human tasks. One framework depicting the augmentation between humans and AI is the delegation framework which theorizes on delegation of tasks between human agents and agentic IS artifacts. However, as the framework is limited to elaborating human agent-agentic IS agent roles, the role of the developer is missing. This study aims to shed light on the role of the developer within the delegation framework. We explore the implication of this involvement through the theoretical lens of adaptive structuration. We use cases drawn from 12 literature sources, qualified by assessing them used against the delegation framework guidelines, and analyzed them to identify developer roles. Our findings show that the developer influences the processes underlying the functioning of the agentic IS artifact, its attributes, its evolution, and mechanisms for delegation. The state of agency in an artifact is influenced and even largely defined by the developer. This implies that agency in an IS artifact can be viewed as encompassing more than their own abilities to act within their environments

Deep Learning, transparency and trust in Human Robot Teamwork

For Autonomous AI systems to be accepted and trusted, the users should be able to understand the reasoning process of the system (i.e., the system should be transparent). Robotics presents unique programming difficulties in that systems need to map from complicated sensor inputs such as camera feeds and laser scans to outputs such as joint angles and velocities. Advances in Deep Neural Networks are now making it possible to replace laborious handcrafted features and control code by learning control policies directly from high dimensional sensor inputs. Because Atari games, where these capabilities were first demonstrated, replicate the robotics problem they are ideal for investigating how humans might come to understand and interact with agents who have not been explicitly programmed. We present computational and human results for making DRLN more transparent using object saliency visualizations of internal states and test the effectiveness of expressing saliency through teleological verbal explanations

A Web Searching Guide: Internet Search Engines & Autonomous Interface Agents Collaboration

The Internet represents the biggest communication media and its dimension increases every day. This continuous growth of information makes the Internet more and more interesting, but also the task of finding selected information becomes more complex and hard. Finding exactly what a user needs is not always an easy task: for example common search engines provide thousands of links for every search. Obviously not all these links are related to what the user really needs. In this paper, we present a Collaborative Autonomous Interface Agent (CAIA) that collaborates with the Internet search engines and supports the user in finding exactly the information consistent with his/her interest. A system has been designed, fully implemented and tested. The testing results shows a big improvement in the relevancy of the retrieved links and of the user’s satisfaction by using CAIA+Google compared to using only Google

Complex Interactions between Multiple Goal Operations in Agent Goal Management

A significant issue in cognitive systems research is to make an agent formulate and manage its own goals. Some cognitive scientists have implemented several goal operations to support this issue, but no one has implemented more than a couple of goal operations within a single agent. One of the reasons for this limitation is the lack of knowledge about how various goals operations interact with one another. This thesis addresses this knowledge gap by implementing multiple-goal operations, including goal formulation, goal change, goal selection, and designing an algorithm to manage any positive or negative interaction between them. These are integrated with a cognitive architecture called MIDCA and applied in five different test domains. We will compare and contrast the architecture\u27s performance with intelligent interaction management with a randomized linearization of goal operations

Complex Interactions between Multiple Goal Operations in Agent Goal Management

A significant issue in cognitive systems research is to make an agent formulate and manage its own goals. Some cognitive scientists have implemented several goal operations to support this issue, but no one has implemented more than a couple of goal operations within a single agent. One of the reasons for this limitation is the lack of knowledge about how various goals operations interact with one another. This thesis addresses this knowledge gap by implementing multiple-goal operations, including goal formulation, goal change, goal selection, and designing an algorithm to manage any positive or negative interaction between them. These are integrated with a cognitive architecture called MIDCA and applied in five different test domains. We will compare and contrast the architecture\u27s performance with intelligent interaction management with a randomized linearization of goal operations