Cooperative multi-agent path planning around a common location has many applications, and has received significant at- tention from the research community. Our research is motivated by the need for groups of autonomous vehicles or mobile robots to collaboratively plan efficient paths around shared navigational coordinates (waypoints) in a distributed and decentralized manner. Our ongoing research is focused on creating a distributed solution to Dresner and Stone’s Autonomous Intersection Management problem. In the future we plan to relax the constraints of this problem, and allow more flexibility in the angles of approach and departure from a single waypoint, and also plan to consider efficient group plans for multi-waypoint routes. In this paper we briefly introduce intersection management, present preliminary results for an unstructured peer-to-peer approach to the problem, and discuss future research directions.

The DARPA Mind’s Eye program seeks to develop in machines a capability that currently exists only in animals: visual intelligence. This paper describes a Neural-Symbolic Cognitive Agent that integrates neural learning, symbolic knowledge representation and temporal reasoning in a visual intelligent system that can reason about actions of entities observed in video. Results have shown that the system is able to learn and represent the underlying semantics of the actions from observation and use this for several visual intelligent tasks, like recognition, description, anomaly detection and gap-filling.

A cognitive robot should construct a plan to attain its goals. While it executes the actions in its plan, it may face several failures due to both internal and external issues. We present a taxonomy to classify these failures that may be encountered during the execution of cognitive tasks. The taxonomy presents a wide range of failure types. To recover from most of these failures presented in this taxonomy, we propose a Robust Planning Framework for cognitive robots. Our framework combines planning, reasoning and learning procedures into each other for robust execution of cognitive tasks. Failures can be detected and handled by reasoning and replanning, respectively. The framework also facilitates learning new hypotheses incrementally based on experience. It can successfully detect and recover from temporary failures on a selected set of actions executed by a Pioneer3DX robot. It has been shown that our preliminary results for hypothesis learning in failure scenarios are promising.

Recently, there has been a growing interest in multiagent path planning (MAPF). Applications include vehicle fleet coordination, computer games, robotics, and various military scenarios. Some researchers have worked at a theoretical level, while others implemented solvers to specific applications. Consequently, similar concepts were developed in different subcommunities, using varying terminology.

In our ongoing work, we aim to control a team of agents soas to achieve a prescribed goal state while being confidentthat collisions with other agents are avoided. Each agent isassociated with a feedback controlled plant, whose continu-ous state trajectories follow some stochastic differential dy-namics. To this end we describe a collision-detection modulebased on a distribution-independent probabilistic bound andemploy a fixed priority method to resolve collisions. Dueto their practical importance, multi-agent collision avoid-ance and control have been extensively studied across differ-ent communities including AI, robotics and control. How-ever, these works typically assume linear and discrete dy-namic models; by contrast, our work intends to overcomethese limitations and to present solutions for continuousstate space. While our current experiments were conductedwith linear stochastic differential equation (SDE) modelswith state-independent noise (yielding Gaussian processes)we believe that our approach could also be applicable to non-Gaussian cases with state-dependent uncertainties.

A* is the algorithm of finding the shortest path between two nodes in a graph. When the searching problem is constituted of a set of linked graphs, A* searches solution like if it is face of one graph formed by linked graphs. While researchers have developed solutions to reduce the execution time of A* in multiple cases by multiples techniques, we develop a new algorithm: DEC-A* which is a decentralized version of A* composing a solution through a collection of graph. A* uses a distance-plus-cost heuristic function to determine the order in which the search visits nodes in the tree. Our algorithm DEC-A* extends the evaluation of the distance-plus-cost heuristic to be the sum of two functions : local distance, which evaluates the cost to reach the nearest neighbor node s to the goal, and global distance which evaluates the cost from s to the goal through other graphs. DEC-A* reduces the time of finding the shortest path and reduces the complexity, while ensuring the privacy of graphs.

Motivated by efficient algorithms for solving combina- torial and discrete instances of the multi-agent pathfinding problem, this report investigates ways to utilize such solutions to solve similar problems in the continuous domain. While a simple discretization of the space which allows the direct application of combinatorial algorithms seems like a straightforward solution, there are additional constraints that such a discretization needs to satisfy in order to be able to provide some form of completeness guarantees in general configuration spaces. This report reviews ideas on how to utilize combinatorial algorithms to solve continuous multi-agent pathfinding problems. It aims to collect feedback from the community regarding the importance and the complexity of this challenge, as well as the appropriateness of the solutions considered here.

We have explored with three notions: conceptualization, and contextualization from situated cognition, and psychic reflection from activity theory for identifying activities into a method called the activity states framework (ASF). The purpose of our work is to build an AI system based on ASF for the identification of collaborators activities during situated context, e.g., collaborators are engaged in a tutorial activity. In this paper, we will introduce and propose how Web-mediated collaborative activities can be identified from collaborators communication exchanges by applying the ASF.

In recent years, major research advances have taken place in the intersection of computer science and economics, but this material has so far been taught primarily at the graduate level. This paper describes a novel semester-long undergraduate-level course in the intersection of computer science and economics at Duke University, titled “CPS 173: Computational Microeconomics.”

A* is often described as being `optimal', in that it expands the minimum number of unique nodes. But, A* may generate many extra nodes which are never expanded. This is a performance loss, especially when the branching factor is large. Partial Expansion A* addresses this problem when expanding a node, n, by generating all the children of n but only storing children with the same f-cost as n. n is re-inserted into the OPEN list, but with the f-cost of the next best child. This paper introduces an enhanced version of PEA* (EPEA*). Given a priori domain knowledge, EPEA* generates only the children with the same f-cost as the parent. EPEA* is generalized to its iterative-deepening variant, EPE-IDA*. For some domains, these algorithms yield substantial performance improvements. State-of-the-art results were obtained for the pancake puzzle and for some multi-agent pathfinding instances. Drawbacks of EPEA* are also discussed.