Dealing with uncertainty in the context of planning has been an active research subject in AI. Addressing the case when uncertainty evolves over time can be difficult. In this work, we provide a solution to this problem by proposing a temporal logic to reason about quantities and probability. For this logic, we provide a PSPACE SAT algorithm together with a complete calculus. The algorithm enables us to perform planning under uncertainty via SAT, extending a technique used for classic planning. We can show that any obtained plan will have certain properties (desired or undesired). The calculus can also be used to derive the impossibility of a plan, given a set of specifications.

In this paper we incorporate integrity constraints in Dung-style abstract argumentation frameworks. We show that even for constraints of a very simple form standard conflict-free semantics for argumentation frameworks are not adequate, as conflicts among arguments should sometimes be accepted and tolerated. For this, we use conflict-tolerant semantics and show how corresponding extensions may be represented in terms of propositional formulas.

Introduction to the papers presented at the 2013 FLAIRS Conference Special Track on Uncertain Reasoning.

This research explores the performance of the iRobot Create machine for optimizing object relocation in an outer space environment. It is an ultimate goal to have it become a symbol of innovation for robots that are sent into outer space. Functioning as a tool-bot, and an active assistant, this robot aims to assist in small duties and respond to commands. With its arm and color blob recognition capabilities, this robot has the potential to receive a request, register and associate it with existing objects in its line of sight, and maneuver the arm to act accordingly, grabbing the correct object and giving it to a worker or engineer. This poster and presentation explains current progress and implementation of the iRobot Create for this purpose.

We consider the multi-robot task allocation (MRTA) problem in an initially unknown environment. The objective of the MRTA problem is to find a schedule or sequence of tasks that should be performed by a set of robots so that the cost or energy expended by the robots is minimized. Existing solutions for the MRTA problem mainly concentrate on finding an efficient task allocation among robots, without directly incorporating changes to tasks' costs originating from changes in robots' paths due to dynamically detected obstacles while moving between tasks. Dynamically updating path costs is an important aspect as changing path costs can alter the task sequence for robots that corresponds to the minimum cost. In this paper, we attempt to address this problem by developing an algorithm called MRTA-RTPP (MRTA with Real-time Path Planning) by integrating a greedy MRTA algorithm for task planning with a Field D*-based path planning algorithm. Our technique is capable of handling dynamic changes in a robot's path costs due to static as well as mobile obstacles and computes a new task schedule if the original schedule is no longer optimal due to the robots' replanned paths. We have verified our proposed technique on physical Corobot robots that perform surveillance-like tasks by visiting a set of locations. Our experimental results show that that our MRTA technique is able to handle dynamic path changes while reducing the cost of the schedule to the robots

In cooperative robot teams, communications can speed up execution, reduce duplication, and prevent interference. Although many systems use explicit communications, persistent intra-team digital communications is not guaranteed. One approach to this challenge is to use implicit communication to infer state rather than using digital messages. We investigate using implicit communication in the form of observation to infer state to coordinate a robot team in a coverage task. We demonstrate how pruning and backtracking a search tree can improve multi-robot coverage. Experiments were conducted to compare team performance of a robot team using observation-based cooperation to one that uses explicit communications.

We report the design and implement of a land robot whose primary task is to “patch” a Wireless Sensor Network. the Tekkotsu/Create system is modified to support GPS-guided navigation and radio-sensing based navigation . The project is a moderate success with both GPS and radio-sensing navigation algorithms achieve similar navigation performance.

With the objective of building robots that accompany humans in daily life, it might be favourable that such robots act humanlike so that humans are able to predict their behaviour without effort. Decision making is one crucial aspect of daily life. As Damasio demonstrated, human decisions are often based on emotions. Earlier work thus developed a decision making framework for artificial intelligent systems based on Damasio’s Somatic Marker Hypothesis and revealed that overall, the decisions made by an artificial agent resemble those of human players. This paper enhances this work in so far that a detailed evaluation of the first 30 decisions made by the modelled agent during this gambling task was done by human subjects. Therefore 26 human participants were recruited who had to evaluate different graphical outputs that visualized the course of the Iowa Gambling Task played by either a modelled agent or a human. The results revealed that participants tend to categorize the course of the game as human, even if it was from the modelled agent. Furthermore, the evaluation of the different courses showed that participants were not able to differentiate between modelled and human output, but they were able to differentiate these from random courses of the game.

Educators who wish to engage their students in multi-agent experimentation and learning need an inexpensive multi-robot system that leverages existing equipment and open-source software. This paper proposes Overwatch as an inexpensive educational tool for teaching and experimenting in multi-robot systems. The interaction of multiple agents within a single environment is an important area of study. It is vital that agents within the environment perceive other agents as intelligent, acting within the environment as cooperative teammates or as competitive members of another team. To do so, the system must meet three goals: first, to allow multiple robots to communicate and coordinate; second, to localize within a shared global coordinate system; third, to recognize their teammates and other teams. The cost and scale of such experimental platforms places them outside the reach of many educational institutions or limits the number of agents that are interacting within the system \cite{Liu201160}. The goal of Overwatch is to create an experimental platform for multi-agent systems that is comprised of much smaller, albeit less capable, robots, many of which are prevalent in academic institutions already. Making use of available open-source libraries and utilizing lower cost robots, such as Scribblers, allows for experiments with many agents. This enables Overwatch to fit into the budget limitations of an academic setting. The Overwatch platform provides the Scribblers with global localization capabilities. This paper presents the system in detail and includes experiments to show its ability to localize, interact with other agents, and coordinate behaviors with these other agents. Additionally, the details to setup this system are also included.

Introduction to the papers presented at the 2013 FLAIRS Conference Special Track on Robotics and Human-Robot Interaction