Representations that combine first-order logic and probability have been the focus of much recent research. Lifted inference algorithms for them avoid grounding out the domain, bringing benefits analogous to those of resolution theorem proving in first-order logic. However, all lifted probabilistic inference algorithms to date treat potentials as black boxes, and do not take advantage of their logical structure. As a result, inference with them is needlessly inefficient compared to the logical case. We overcome this by proposing the first lifted probabilistic inference algorithm that exploits determinism and context specific independence. In particular, we show that AND/OR search can be lifted by introducing POWER nodes in addition to the standard AND and OR nodes. Experimental tests show the benefits of our approach.

We consider the problem of discrete multi-label entity classification in images. We argue that the framework of Markov Logic can provide a unified, well-grounded mechanism to incorporate arbitrary logical relationships between entities to improve classification in images, and thus generalizes much of the recent work on exploiting local and global context in object recognition and scene understanding. Furthermore, we show that Markov Logic can provide a powerful new set of contexts that can relate entities across images in a database for joint classification of all entities in a test set simultaneously. We relate this collective classification of images to graph-based semi-supervised learning approaches, and show that Markov Logic can effectively provide a method to unify context-related work with semi-supervised approaches in a way that neither techniques could easily do on their own. Finally, we show the efficacy of these techniques on a face recognition task on three datasets showing that adding contextual relations dramatically improves accuracy over semi-supervised learning approaches alone.

Activity recognition plays an important role in many areas such as smart environments by offering unprecedented opportunities for assisted living, automation, security and energy efficiency. It’s also an essential component for planning and plan recognition in smart environments. One challenge of activity recognition is the need for collecting and annotating huge amounts of data for each new physical setting in order to be able to carry out the conventional activity discovery and recognition algorithms. This extensive initial phase of data collection and annotation results in a prolonged installation process and excessive time investment for each new space. In this paper we propose a new method of transferring learned knowledge of activities to a new physical space in order to leverage the learning process in the new environment. Our method called ”Home to Home Transfer Learning” (HHTL) is based on using a semi EM framework and modeling activities using structural, temporal and spatial features. This method allows us to avoid the tedious task of collecting and labeling huge amounts of data in the target space, and allows for a more accelerated and more scalable deployment cycle in the real world. It also allows us to exploit the insights learned in previous spaces. To validate our algorithms, we use the data collected in several smart apartments with different physical layouts.

In Real-Time Strategy (RTS) video games, players (controlled by humans or computers) build structures and recruit armies, fight for space and resources in order to control strategic points, destroy the opposing force and ultimately win the game. Players need to predict where and how the opponents will strike in order to best defend themselves. Conversely, assessing how the opponents will defend themselves is crucial to mounting a successful attack while exploiting the vulnerabilities in the opponent's defence strategy. In this context, to be truly adaptable, computer-controlled players need to recognize their opponents' behaviour, their goals, and their plans to achieve those goals. In this paper we analyze the algorithmic challenges behind behaviour recognition in RTS games and discuss a generic RTS behaviour recognition system that we are developing to address those challenges. The application domain is that of RTS games, but many of the key points we discuss also apply to other video game genres such as multiplayer first person shooter (FPS) games.

Previous work on the YAPPR plan recognition system provided algorithms for translating conventional HTN plan libraries into lexicalized grammars and treated the problem of plan recognition as one of parsing. To produce these grammars required a fixed bound for any loops within the grammar and a presented a problem for optional actions within HTN plans. In this work we show that well known transformations from formal language theory can be used to rewrite the plan grammars to remove these limitations on the plan libraries.

We present a formal approach for combining programming by demonstration (PbD) with programming by instruction (PbI) — a largely unsolved problem. Our solution is based on the integration of two successful formalisms: version space algebras and the logic programming language Golog. Version space algebras have been successfully applied to programming by demonstration. Intuitively, a version space describes a set of candidate procedures and a learner filters this space as necessary to be consistent with all given demonstrations of the target procedure. Golog, on the other hand, is a logical programming language defined in the situation calculus that allows for the specification of non-deterministic programs. While Golog was originally proposed as a means for integrating programming and automated planning, we show that it serves equally well as a formal framework for integrating PbD and PbI. Our approach is the result of two key insights: (a) Golog programs can be used to define version spaces, and (b) with only a minor augmentation, the existing Golog semantics readily provides the update-function for such version spaces, given demonstrations. Moreover, as we will show, two or more programs can be symbolically synchronized, resulting in the intersection of two, possibly infinite, version spaces. The framework thus allows for a rather flexible integration of PbD and PbI, and in addition establishes a new connection between two active research areas, enabling cross-fertilization.

A metareasoning problem involves three parts: 1) a set of concrete problem domains; 2) reasoners to reason about the problems; and, 3) metareasoners to reason about the reasoners. We believe that the metareasoning community would benefit from agreeing on the first two problems. To support this kind of collaboration, we offer an open source 3D simulator containing everyday, commonsense problems that take place in kitchens. This paper presents several arguments for using a simulator to solve commonsense problems. The paper concludes by describing future work in simulator-based unified generative benchmarks for AI.

Many approaches were explored in recent years to introduce principles of metacognition and meta-learning into cognitive architectures, yet none of them resulted in a scalable human-like learner. This work presents an approach intended to fill the gap between human self-regulated learners and artificial learners by introducing a new spin of the familiar core cognitive architecture paradigm, taking it to a meta-level. The resultant architecture enables in artifacts exclusively human higher cognitive and learning abilities: specifically, deliberative new knowledge construction. Model predictions agree with results of a pilot study with human subjects.

It is still unclear what metacognition is. Two main theories about metacognition are reviewed, each of which claims to provide a better explanation of the phenomenon, while discrediting the other theory as inappropriate. My claim is that in order to do justice to the complex phenomenon of metacognition, we must distinguish two levels of this capacity. It can be shown that each of these theories has been trying to explain only one of the two levels and that, consequently, the conflict between them can be dissolved. Finally, I characterize each level and explain some of their interactions.

It is crucial for social systems to adapt to the dynamics of open environments. This adaptation process becomes especially challenging in the context of multiagent systems. In this paper, we argue that multiagent meta-level control is an effective way to determine when this adaptation process should be done and how much effort should be invested in adaptation as opposed to continuing with the current action plan. We develop a reinforcement learning based mechanism for multiagent meta-level control that facilitates the metalevel control component of each agent to learn policies in a decentralized fashion that (a) it can efficiently support agent interactions with other agents and (b) reorganize the underlying network when needed. We evaluate this mechanism in the context of a multiagent tornado tracking application called NetRads. Empirical results show that adaptive multiagent meta-level control significantly improves the performance of the tornado tracking network for a variety of weather scenarios.