Traditionally, in the fields of artificial intelligence and robotics, representations of the self have been conspicuously absent. Capabilities of systems are listed explicitly by developers during construction and choices between behavioral options are decided based on search, inference, and planning. In robotics, while knowledge of the external world has often been acquired through experience, knowledge about the robot itself has generally been built in by the designer. Built-in models of the robot's kinematics, physical and sensory capabilities, and other equipment have stood in the place of self-knowledge, but none of these representations offer the flexibility, robustness, and functionality that are present in people. In this work, we seek to emulate forms of self-awareness developed during human infancy in our humanoid robot, Nico. In particular, we are interested in the ability to reason about the robot's embodiment and physical capabilities, with the robot building a model of itself through its experiences.

In this paper we explore challenges related to the engineering of plan libraries for plan recognition. This is an often overlooked problem, yet essential in the design of any real world plan recognizers. We mainly discuss challenges related to the evaluation of equivalence between plan libraries. We explain why this is a potential source of ill-conceived plan libraries. We consider an existing well-established probabilistic plan recognizer as vehicle for our discussion, using the formalism of probabilistic hierarchical task networks to represent plans. We propose avenues for exploring solutions to those challenges within that framework.

Learning useful information when solving SAT or CSP problems to speed up a tree-search approaches, is one of the main explored tracks in various works. Such information are known as goods and nogoods and they aim to forbid to repetitively visit the same parts of the search space. Unfortunately and unlike nogoods, the exploitation of goods is limited to tree-search approaches based on the structural properties of the problem. In this paper, we propose to generalize and reformulate structural goods under SAT. We also propose a learning scheme of general goods and show their integration in a DPLL-like procedure.

Open Mind Common Sense (OMCS) is a freely available crowd-sourced knowledge base of natural language statements about the world. The goal of Open Mind Common Sense is to provide intuition to AI systems and applications by giving them access to a broad collection of basic information and the computational tools to work with this data. For our system demo, we will be presenting three aspects of the OMCS project: the OMCS knowledge base, the Concept-Net semantic network (Liu and Singh 2004) (Havasi, Speer, and Alonso 2007), and the AnalogySpace algorithm (Speer, Havasi, and Lieberman 2008) which deals well with noisy, user-contributed data. Figure 1: AnalogySpace discovers patterns in common sense Open Mind Common Sense takes a distributed approach knowledge and uses them for inference. The project allows the general public to enter commonsense score to indicate its reliability, which increases either when knowledge into it, without requiring any knowledge a contributor votes for a statement through our Web site of linguistics, artificial intelligence, or computer science.The or when multiple contributors submit equivalent statements OMCS has been collecting commonsense statements from independently.

Providing energy conservation services becomes a hot research topic because more and more people attach importance to environmental protection. This research proposes a framework that consists of four process models: appliance recognition, activity-appliances model, unattended appliances detection, and energy conservation service. Appliance recognition model can recognizes the operating states of appliances from raw sensing data of electric power. An activity-appliances model has been built to associate activities with appliances according to the data of Open Mind Common Sense Project. Using the relationship between activities can help to detect unattended appliances, which are consuming electric power but not take part in the resident’s activities. After obtain information of appliance operating states and unattended appliances, residents can receive energy conservation services for notifying the energy consumption information. Finally, the experimental results show that dynamic Baysian network approach can achieve higher than 92% accuracy for appliance recognition. Data of activity-appliances model shows most appliances are strong activity-related.

Machine reading aims to automatically extract knowledge from text. It is a long-standing goal of AI and holds the promise of revolutionizing Web search and other fields. In this paper, we analyze the core challenges of machine reading and show that statistical relational AI is particularly well suited to address these challenges. We then propose a unifying approach to machine reading in which statistical relational AI plays a central role. Finally, we demonstrate the promise of this approach by presenting OntoUSP, an end-to-end machine reading system that builds on recent advances in statistical relational AI and greatly outperforms state-of-the-art systems in a task of extracting knowledge from biomedical abstracts and answering questions.

The starting point is an ongoing effort to structure underlying intelligent behavior, whether intended reconstruct cognitive architectures from the ground up via as models of human intelligence and/or implementations of graphical models (Koller and Friedman 2009), with the artificial intelligence (Langley, Laird and Rogers 2009). A aim of understanding existing architectures better, basic cognitive architecture may comprise memories, exploring the overall space of architectures, and decision algorithms, learning mechanisms, and some developing new and improved architectures (Rosenbloom means of interacting with external environments.

Since the expert systems movement of the 1980s and 1990s, - Joint inference between expert knowledge and general AI has had the dream of reproducing expert behavior in specialized Commonsense background knowledge; domains of knowledge, such as medicine or engineering, - Efficient inference, both forward and backward, of plausible by collecting knowledge from human experts. But assertions. the first generations of expert systems suffered from two problems -- first, the difficulty of knowledge engineering

Informational conflicts in operational policies cause agents to run into situations where responding based on the rules in one policy violates the same or another policy. Static checking of these conflicts is infeasible and impractical in a dynamic environment. This paper discusses a practical approach to handling policy conflicts in real-time domains within the context of a hierarchical military command and control simulated system that consists of a central command, squad leaders and squad members. All the entities in the domain function according to preset communication and action protocols in order to perform successful missions. Each entity in the domain is equipped with an instance of a metacognitive component to provide on-board/on-time analysis of actions and recommendations during the operation of the system. The metacognitive component is the Metacognitive Loop (MCL) which is a general purpose anomaly processor designed to function as a cross-domain plugin system. It continuously monitors expectations and notices when they are violated, assesses the cause of the violation and guides the host system to an appropriate response. MCL makes use of three ontologies—indications, failures and responses—to perform the notice, assess and guide phases when a conflict occurs. Conflicts in the set of rules (within a policy or between policies) manifest as expectation violations in the real world. These expectation violations trigger nodes in the indication ontology which, in turn, activate associated nodes in the failure ontology. The responding failure nodes then activate the appropriate nodes in the response ontology. Depending on which response node gets activated, the actual response may vary from ignoring the conflict to prioritizing, modifying or deleting one or more conflicting rules.

The impressive results of the 2007 DARPA Urban Challenge showed that fully autonomous vehicles are technologically feasible with current intelligent vehicle hardware. It is natural to ask how current transportation infrastructure can be improved when most vehicles are driven autonomously in the future. Dresner and Stone proposed a new intersection control mechanism called Autonomous Intersection Management (AIM) and showed in simulation that intersection control can be made more efficient than the traditional control mechanisms such as traffic signals and stop signs. In this paper, we extend the study by examining the relationship between the precision of cars' motion controllers and the efficiency of the intersection controller. We propose a planning-based motion controller that can reduce the chance that autonomous vehicles stop before intersections, and show that this controller can increase the efficiency of the intersection control mechanism.