The rectangle-packing problem consists of finding an enclosing rectangle of smallest area that can contain a given set of rectangles without overlap. Our new benchmark includes rectangles of successively higher precision, challenging the previous state-of-the-art, which enumerates all locations for placing rectangles, as well as all bounding box widths and heights up to the optimal box. We instead limit the rectangles’ coordinates and bounding box dimensions to the set of subset sums of the rectangles’ dimensions. We also dynamically prune values by learning from infeasible subtrees and constrain the problem by replacing rectangles and empty space with larger rectangles. Compared to the previous state-of-the-art, we test 4,500 times fewer bounding boxes on the high-precision benchmark and solve N =9 over two orders of magnitude faster. We also present all optimal solutions up to N =15, which requires 39 bits of precision to solve. Finally, on the open problem of whether or not one can pack a particular infinite series of rectangles into the unit square, we pack the first 50,000 such rectangles witha greedy heuristic and conjecture that the entire infinite series can fit..

We present a first theoretical analysis of the power of polynomial-time preprocessing for important combinatorial problems from various areas in AI. We consider problems from Constraint Satisfaction, Global Constraints, Satisfiability, Nonmonotonic and Bayesian Reasoning. We show that, subject to a complexity theoretic assumption, none of the considered problems can be reduced by polynomial-time preprocessing to a problem kernel whose size is polynomial in a structural problem parameter of the input, such as induced width or backdoor size. Our results provide a firm theoretical boundary for the performance of polynomial-time preprocessing algorithms for the considered problems.

The hypothesis is presented that "cognitive synergy" -- proactive and mutually-assistive feedback between different cognitive processes associated with different types of memory -- may serve as a foundation for advanced artificial general intelligence. A specific AI architecture founded on this idea, OpenCogPrime, is described, in the context of its application to control virtual agents and robots. The manifestations of cognitive synergy in OpenCogPrime's procedural and declarative learning algorithms are discussed in some detail.

We have carefully instrumented a large portion of the population living in a university graduate dormitory by giving participants Android smart phones running our sensing software. In this paper, we propose the novel problem of predicting mobile application (known as “apps”) installation using social networks and explain its challenge. Modern smart phones, like the ones used in our study, are able to collect different social networks using built-in sensors. (e.g. Bluetooth proximity network, call log network, etc) While this information is accessible to app market makers such as the iPhone AppStore, it has not yet been studied how app market makers can use these information for marketing research and strategy development. We develop a simple computational model to better predict app installation by using a composite network computed from the different networks sensed by phones. Our model also captures individual variance and exogenous factors in app adoption. We show the importance of considering all these factors in predicting app installations, and we observe the surprising result that app installation is indeed predictable. We also show that our model achieves the best results compared with generic approaches.

We address the problem of transferring information learned from experiments to a different environment, in which only passive observations can be collected. We introduce a formal representation called "selection diagrams" for expressing knowledge about differences and commonalities between environments and, using this representation, we derive procedures for deciding whether effects in the target environment can be inferred from experiments conducted elsewhere. When the answer is affirmative, the procedures identify the set of experiments and observations that need be conducted to license the transport. We further discuss how transportability analysis can guide the transfer of knowledge in non-experimental learning to minimize re-measurement cost and improve prediction power.

In many multi-agent applications such as distributed sensor nets, a network of agents act collaboratively under uncertainty and local interactions. Networked Distributed POMDP (ND-POMDP) provides a framework to model such cooperative multi-agent decision making. Existing work on ND-POMDPs has focused on offline techniques that require accurate models, which are usually costly to obtain in practice. This paper presents a model-free, scalable learning approach that synthesizes multi-agent reinforcement learning (MARL) and distributed constraint optimization (DCOP). By exploiting structured interaction in ND-POMDPs, our approach distributes the learning of the joint policy and employs DCOP techniques to coordinate distributed learning to ensure the global learning performance. Our approach can learn a globally optimal policy for ND-POMDPs with a property called groupwise observability. Experimental results show that, with communication during learning and execution, our approach significantly outperforms the nearly-optimal non-communication policies computed offline.

That drone and its basic capabilities are summarized in Figure 1. The flexibility and controllability of quadrotor helicopters have made them a recent focus of interest among robotics and AI research groups. At the same time, their popularity has led to a wide range of commercially available platforms, some at prices accessible for undergraduate educational use. This project evaluates the ARDrone quadrotor helicopter as a basis for use in undergraduate classes such as robotics, computer vision, or embodied AI. We have encountered both successes and frustrations in using the ARDrone to date. Looking forward, the quadrotor's capabilities do seem a promising basis for future curricular offerings.

We propose influence games, a new class of graphical games, as a model of the behavior of large but finite networked populations. Grounded in non-cooperative game theory, we introduce a new approach to the study of influence in networks that captures the strategic aspects of complex interactions in the network. We study computational problems on influence games, including the identification of the most influential nodes. We characterize the computational complexity of various problems in influence games, propose several heuristics for the hard cases, and design approximation algorithms, with provable guarantees, for the most influential nodes problem.

The differential heuristic (DH) is an effective memory-based heuristic for explicit state spaces. In this paper we aim to improve its performance and memory usage. We introduce a compression method for DHs which stores only a portion of the original uncompressed DH, while preserving enough information to enable efficient search. Compressed DHs (CDH) are flexible and can be tuned to fit any size of memory, even smaller than the size of the state space. Furthermore, CDHs can be built without the need to create and store the entire uncompressed DH. Experimental results across different domains show that, for a given amount of memory, a CDH significantly outperforms an uncompressed DH.

Ordinal regression is an important research topic in machine learning. It aims to automatically determine the implied rating of a data item on a fixed, discrete rating scale. In this paper, we present a novel ordinal regression approach via manifold learning, which is capable of uncovering the embedded nonlinear structure of the data set according to the observations in the highdimensional feature space. By optimizing the order information of the observations and preserving the intrinsic geometry of the data set simultaneously, the proposed algorithm provides the faithful ordinal regression to the new coming data points. To offer more general solution to the data with natural tensor structure, we further introduce the multilinear extension of the proposed algorithm, which can support the ordinal regression of high order data like images. Experiments on various data sets validate the effectiveness of the proposed algorithm as well as its extension.