Parsing human poses in images is fundamental in extracting critical visual information for artificial intelligent agents. Our goal is to learn self-contained body part representations from images, which we call visual symbols, and their symbol-wise geometric contexts in this parsing process. Each symbol is individually learned by categorizing visual features leveraged by geometric information. In the categorization, we use Latent Support Vector Machine followed by an efficient cross validation procedure. Then, these symbols naturally define geometric contexts of body parts in a fine granularity for effective inference. When the structure of the compositional parts is a tree, we derive an efficient approach to estimating human poses in images. Experiments on two large datasets suggest our approach outperforms state of the art methods.

Most existing work on learning planning models assumes that the entire model needs to be learned from scratch. A more realistic situation is that the planning agent has an incomplete model which it needs to refine through learning. In this paper we propose and evaluate a method for doing this. Our method takes as input an incomplete model (with missing preconditions and effects in the actions), as well as a set of plan traces that are known to be correct. It outputs a refined model that not only captures additional precondition/effect knowledge about the given actions, but also macro actions. We use a MAX-SAT framework for learning, where the constraints are derived from the executability of the given plan traces, as well as the preconditions/effects of the given incomplete model. Unlike traditional macro-action learners which use macros to increase the efficiency of planning (in the context of a complete model), our motivation for learning macros is to increase the accuracy (robustness) of the plans generated with the refined model. We demonstrate the effectiveness of our approach through a systematic empirical evaluation.

The recent proliferation of smart-phones and other wearable devices has lead to a surge of new mobile applications. Partially observable Markov decision processes provide a natural framework to design applications that continuously make decisions based on noisy sensor measurements. However, given the limited battery life, there is a need to minimize the amount of online computation. This can be achieved by compiling a policy into a finite state controller since there is no need for belief monitoring or online search. In this paper, we propose a new branch and bound technique to search for a good controller. In contrast to many existing algorithms for controllers, our search technique is not subject to local optima. We also show how to reduce the amount of search by avoiding the enumeration of isomorphic controllers and by taking advantage of suitable upper and lower bounds. The approach is demonstrated on several benchmark problems as well as a smart-phone application to assist persons with Alzheimer's to wayfind.

We focus on solving two-player zero-sum extensive-form games with perfect information and simultaneous moves. In these games, both players fully observe the current state of the game where they simultaneously make a move determining the next state of the game. We solve these games by a novel algorithm that relies on two components: (1) it iteratively solves the games that correspond to a single simultaneous move using a double-oracle method, and (2) it prunes the states of the game using bounds on the sub-game values obtained by the classical Alpha-Beta search on a serialized variant of the game. We experimentally evaluate our algorithm on the Goofspiel card game, a pursuit-evasion game, and randomly generated games. The results show that our novel algorithm typically provides significant running-time improvements and reduction in the number of evaluated nodes compared to the full search algorithm.

The combined approach of the Qualitative Reasoning and Probabilistic Functions for the knowledge representation is proposed. The method aims at represent uncertain, qualitative knowledge that is essential for the moving blocks task's execution. The attempt to formalize the commonsense knowledge is performed with the Situation Calculus language for reasoning and robot's beliefs representation. The method is implemented in the Prolog programming language and tested for a specific simulated scenario. In most cases the implementation enables us to solve a given task, i.e., move blocks to desired positions. The example of robot's reasoning and main parts of the implemented program's code are presented.

Annals of Operations Research manuscript No. (will be inserted by the editor) Abstract The Low Autocorrelation Binary Sequence problem has applications in telecommunications, is of theoretical interest to physicists, and has inspired work by many optimisation researchers because of its difficulty. For many years it was considered unsuitable for solution by metaheuristics because of its search space topology, but in recent years metaheuristics have found long high-quality sequences. However, complete search has not progressed since a parallel branch-and-bound method of 1996. In this paper we find four ways of improving branch-and-bound, leading to a tighter relaxation, faster convergence to optimality and better scalability. We also extend known optimality results for skew-symmetric sequences from length 73 to 89.

Associative memories store content in such a way that the content can be later retrieved by presenting the memory with a small portion of the content, rather than presenting the memory with an address as in more traditional memories. Associative memories are used as building blocks for algorithms within database engines, anomaly detection systems, compression algorithms, and face recognition systems. A classical example of an associative memory is the Hopfield neural network. Recently, Gripon and Berrou have introduced an alternative construction which builds on ideas from the theory of error correcting codes and which greatly outperforms the Hopfield network in capacity, diversity, and efficiency. In this paper we implement a variation of the Gripon-Berrou associative memory on a general purpose graphical processing unit (GPU). The work of Gripon and Berrou proposes two retrieval rules, sum-of-sum and sum-of-max. The sum-of-sum rule uses only matrix-vector multiplication and is easily implemented on the GPU. The sum-of-max rule is much less straightforward to implement because it involves non-linear operations. However, the sum-of-max rule gives significantly better retrieval error rates. We propose a hybrid rule tailored for implementation on a GPU which achieves a 880-fold speedup without sacrificing any accuracy.

The problem of optimizing unknown costly-to-evaluate functions has been studied for a long time in the context of Bayesian Optimization. Algorithms in this field aim to find the optimizer of the function by asking only a few function evaluations at locations carefully selected based on a posterior model. In this paper, we assume the unknown function is Lipschitz continuous. Leveraging the Lipschitz property, we propose an algorithm with a distinct exploration phase followed by an exploitation phase. The exploration phase aims to select samples that shrink the search space as much as possible. The exploitation phase then focuses on the reduced search space and selects samples closest to the optimizer. Considering the Expected Improvement (EI) as a baseline, we empirically show that the proposed algorithm significantly outperforms EI.

We analyze several of the key technical and practical challenges involved in representing activity context across a large variety of knowledge, components, and applications. We present two novel broad methods that enable semantic knowledge capture and interchange, and suggest how they can be used for activity context-awareness. The first is knowledge representation and reasoning (KRR) in Rulelog, an expressively extended form of declarative logic programs that features defeasible higher-order logic formulas yet is computationally tractable, and is a draft dialect of W3C RIF. Rulelog's expressiveness enables representation of exceptions and change, and thus processes, agreements, and policies, e.g., for confidentiality. The second broad method is Textual Logic, an approach to mapping between natural language (text) and logic, where the mapping itself is logic-based. Textual Logic leverages Rulelog's expressiveness to enable relatively rapid text-based authoring of rich knowledge, reducing the knowledge acquisition bottleneck. Together, Rulelog and Textual Logic help address the potential for ontological and KRR Babel that lurks when representing activity context using previous semantic technologies.

Getting an embodied cognitive system to form a mental model of its world is a challenging prospect. Most AI systems leverage domains defined entirely by the system designers—initial objects, relations, operations, and even search control knowledge are often pre-specified. Building autonomous systems that can bootstrap themselves using a minimal domain definition is a critical research objective of Developmental AI. PAM-P2, a domain agnostic cognitive system, builds a world model using an initial set of user specified primitive actions and homeostatic needs. As sensory datasets are received, an ontology is formed and used to derive situations, events, episodes, solutions, problems, and predictions. An overview of the PAM-P2 architecture and knowledge representation is presented.