We consider the problem of designing automated strategies for interactions with human subjects, where the humans must be rewarded for performing certain tasks of interest. We focus on settings where there is a single task that must be performed many times by different humans (e.g. answering a questionnaire), and the humans require a fee for performing the task. In such settings, our objective is to minimize the average cost for effectuating the completion of the task. We present two automated strategies for designing efficient agents for the problem, based on two different models of human behavior. The first, the Reservation Price Based Agent (RPBA), is based on the concept of a reservation price, and the second, the No Bargaining Agent (NBA), uses principles from behavioral science. The performance of the agents has been tested in extensive experiments with real human subjects, where NBA outperforms both RPBA and strategies developed by human experts.

In aviation industry, free flight is a new concept which implies considering more freedom in the selection and modification of flight paths during flight time. The free flight concept allows pilots choose their own flight paths more efficient, and also plan for their flight with high performance. Although free flight has many advantages such as minimum delays and the reduction of the workload of the air traffic control centers, this concept causes many problems which one of the most important of them are conflicts between different aircrafts. Thus, Conflict Detection and Resolution (CD&R) is a major challenge in air traffic management. In this paper, we presented a model for CD&R between aircrafts in air traffic management using Graph Coloring Problem (GCP) method. In fact, we mapped the congestion area to a corresponding graph, and then addressed to find a reliable and optimal coloring for this graph using one of the new evolutionary algorithms known as Imperialist Competitive Algorithm (ICA) to solve the conflicts. Using ICA for solving GCP is a new method.

Smart cities and smart grids heavily depend on data being exchanged between a large number of heterogeneous entities. Parts of the data which such systems depend on are relevant to the privacy of individuals, e.g., data about energy consumption or current location. We assume the use of semantic technologies for data representation and exchange, and express privacy requirements as formal policies. We take a data-centric view, that is, we attach policies that restrict isolated uses of data to the data directly. When systems exchange data, they also exchange the policies pertaining to the exchanged data. The main benefit of such an approach over a system-level view is that our data-centric approach works in scenarios without central control.

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.

We present a model of unsupervised learning in the hybrid SAL (Synthesis of ACT-R and Leabra) architecture. This model follows the hypothesis that higher evaluative cognitive mechanisms can serve to provide training signals for perceptual learning. This addresses the problem that supervised learning seems necessary for strong perceptual performance, but explicit feedback is rare in the real world and difficult to provide for artificial learning systems. The hybrid model couples the perceptual strengths of Leabra with ACT-R's cognitive mechanisms, specifically its declarative memory, to evolve its own symbolic representations of objects encountered in the world. This is accomplished by presenting the objects to the Leabra visual system and committing the resulting representation to ACT-R's declarative memory. Subsequent presentations are either recalled as instances of a previous object category, in which case the positive association with the representation is rehearsed by Leabra, or they cause ACT-R to generate new category labels, which are also subject to the same rehearsal. The rehearsals drive the network's representations to convergence for a given category; at the same time, rehearsals on the ACT-R side reinforce the chunks that encode the associations between representation and label. In this way, the hybrid model bootstraps itself into learning new categories and their associated features; this framework provides a potential approach to solving the symbol grounding problem. We outline the operations of the hybrid model, evaluate its performance on the CU3D-100 (cu3d.colorado.edu) image set, and discuss further potential improvements to the model, including the integration of motor functions as a way of providing an internal feedback signal to augment and guide a purely bottom-up unsupervised system.

Problems that require multiple agents to follow non-interfering paths from their current states to their respective goal states are called multi-agent pathfinding problems (MAPFs). In previous work, we presented Independence Detection (ID), an algorithm for breaking a large MAPF problem into smaller problems that can be solved independently. Independence Detection is complete and can be used in combination with both optimal and approximation algorithms. This paper serves as an introduction to Independence Detection and aims to clarify its details.

Collision avoidance and multi-agent navigation is an important component of modern video games. Recent developments in commodity hardware, in particular the utilization of multi-core and many-core architectures in personal computers and consoles are allowing large numbers of virtual agents to be incorporated into game levels in increasing numbers. We present the hybrid reciprocal velocity obstacle and optimal reciprocal collision avoidance methods for reciprocal collision avoidance and navigation in video games and described their implementations in C++ as HRVO Library and RVO2 Library. The libraries can efficiently simulate groups of twenty-five to one thousand virtual agents in dense conditions and around moving and static obstacles.