As many cities around the world provide access to raw public data along the Open Data movement, many questions arise concerning the accessibility of these data. Various data formats, duplicate identifiers, heterogeneous metadata schema descriptions, and diverse means to access or query the data exist. These factors make it difficult for consumers to reuse and integrate data sources to develop innovative applications. The Semantic Web provides a global solution to these problems by providing languages and protocols for describing and accessing datasets. This paper presents Datalift, a framework and a platform helping to lift raw data sources to semantic interlinked data sources.

The average home computer user needs help in reducing the security risk of their home computer. We are working on an alternative approach from current home security software in which a software agent helps a user manage his/her security risk. Planning is integral to the design of this agent in several ways. First, planning can be used to make the underlying security model manageable by generating attack paths to identify vulnerabilities that are not a problem for a particular user/home computer. Second, planning can be used to identify interventions that can either avoid the vulnerability or mitigate the damage should it occur. In both cases, a central capability is that of generating alternative plans so as to find as many possible ways to trigger the vulnerability and to provide the user with options should the obvious not be acceptable. We describe our security model and our state-based approach to generating alternative plans. We show that the state-based approach can generate more diverse plans than a heuristic-based approach. However, the state-based approach sometimes generates this diversity with better quality at higher search cost.

We are working to develop plan critiquing methods where a planner is used to identify flaws in an existing plan, in order to provide assistance to human planners. In this paper, we describe how to use any classical planning algorithm for verification and counterexample generation for plans already generated by some agent (human or an automated planning system). We show how to take an original classical planning domain, problem, and plan, and a set of uncontrollable (disturbance) actions and agents, and compile those inputs into a new "counter-planning'' problem. This counter-planning problem can be given to an arbitrary PDDL planner, in order to generate counterexample traces where uncontrollable actions can upset plan execution. Our experiments with a large set of planning problems in two multi-agent, dynamic planning domains demonstrated that our approach can verify a plan or generate a counterexample quickly and reliably. We have also compared our approach with a state-of-the-art model-checking system: the results suggest that using classical planners for generating counter plans is more promising than model-checking based verification.

In this study, we propose a method for learning interactions among different types of objects to devise new plans using these objects. Learning is accomplished by observing a given sequence of events with their timestamps and using spatial information on the initial state of the objects in the environment. We assume that no intermediate state information is available about the states of objects. We have used the Incredible Machine game as a suitable domain for analyzing and learning object interactions. When a knowledge base about relations among objects is provided, interactions to devise new plans are learned to a desired extent. Moreover, using spatial information of objects or temporal information of events makes it feasible to learn the conditional effects of objects on each other. Our analyses show that, integrating spatial and temporal data in a spatio-temporal learning approach gives closer results to that of the knowledge-based approach by providing applicable event models for planning. This is promising because gathering spatio-temporal information does not require great amount of knowledge.

Many practical planning problems necessitate the generation of a plan under incomplete information about the state of the world. In this paper we propose the notion of Assumption-Based Planning. Unlike conformant planning, which attempts to find a plan under all possible completions of the initial state, an assumption-based plan supports the assertion of additional assumptions about the state of the world, simplifying the planning problem. In many practical settings, such plans can be of higher quality than conformant plans. We formalize the notion of assumption-based planning, establishing a relationship between assumption-based and conformant planning, and prove properties of such plans. We further provide for the scenario where some assumptions are more preferred than others. Exploiting the correspondence with conformant planning, we propose a means of computing assumption-based plans via a translation to classical planning. Our translation is an extension of the popular approach proposed by Palacios and Geffner and realized in their T0 planner. We have implemented our planner, A0, as a variant of T0 and tested it on a number of expository domains drawn from the International Planning Competition. Our results illustrate the utility of this new planning paradigm.

For the problem of moving a set of agents on a connected graphto agent-specific goal locations, free of collisions, we propose a multiflow based integer linear programming (ILP) model that finds a time optimal solution. The resulting algorithm from our ILP model is complete and guarantees to yield true optimal solutions. Focusing on the time optimal formulation, we evaluate its performance, both as a stand alone algorithm and as a generic heuristic for quickly solving large problem instances. The computational results confirm the effectiveness of our method.

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.

In this paper we present a formal approach to reciprocal collision avoidance for multiple mobile robots sharing a common 2-D or 3-D workspace whose dynamics are subject to linear differential constraints. Our approach defines a protocol for robots to select their control input independently (i.e. without coordination with other robots) while guaranteeing collision-free motion for all robots, assuming the robots can perfectly observe each other's state. To this end, we use the concept of LQR-Obstacles that define sets of forbidden control inputs that lead a robot to collision with obstacles, and extend it for reciprocal collision avoidance among multiple robots. We implemented and tested our approach in 3-D simulation environments for reciprocal collision avoidance of quadrotorhelicopters, which have complex dynamics in 16-D state spaces. Our results suggest that our approach avoids collisions among over a hundred quadrotors in tight workspaces at real-time computation rates.

This poster proposes a human computation approach to collecting representative pictures for words so that the collected pictures can efficiently and effectively convey the meaning of the words or concepts. A large collection of representative pictures can be used in text-to-picture communication systems, and may also be used to teach computers to learn what representative pictures are. We have developed a web application to help players of Draw Something, a popular social mobile game, search pictures for drawing inspiration while at the same time they implicitly help us collect representative pictures for words. Our preliminary result shows that the proposed approach has the potential to harvest Draw Something players for collecting desired data.

A natural, common way of doing this is by crowdsourcing this stage as well, and specifically Human computation is a fast-growing field that seeks to harness letting people vote over different proposals that were the relative strengths of humans to solve problems that submitted by their peers. For example, in EteRNA thousands are difficult for computers to solve alone. The field has recently of designs are submitted each month, but only a small number been gaining traction within the AI community, as k of them can be synthesized in the lab (as of late 2011, increasingly more deep connections between AI and human k 8). To single out k designs to be synthesized, players computation are uncovered (Dai, Mausam, and Weld 2010; vote by reporting their k favorite designs, each of which is Shahaf and Horvitz 2010).