Building an intelligent agent, which simulates human-level learning appropriate for learning math, science, or a second language, could potentially benefit both education in understanding human learning, and artificial intelligence in creating human-level intelligence. Recently, we have proposed an efficient approach to acquiring procedural knowledge using transfer learning. However, it operated as a separate module. In this paper, we describe how to integrate this module into a machine-learning agent, SimStudent, that learns procedural knowledge from examples and through problem solving. We illustrate this method in the domain of algebra, after which we consider directions for future research in this area.

The hardness of finite domain Constraint Satisfaction Problems (CSPs) is an important research topic in Constraint Programming (CP) community. In this paper, we study the association rule mining techniques together with rule deduction and propose a cascaded approach to extract interesting patterns of hard CSPs with functional constraints. Specifically, we generate random CSPs, collect controlling parameters and hardness characteristics by solving all the CSP instances. Afterwards, we apply association rule mining with rule deduction on the collected data set and further extract interesting patterns of the hardness of the randomly generated CSPs. As far as we know, this problem is investigated with data mining techniques for the first time.

The ability to discover the effects of actions and apply this knowledge during goal-oriented action selection is a fundamental requirement of embodied intelligent agents. In our ongoing work, we hope to demonstrate the utility of learned control models for whole-body mobile manipulation. In this short paper we discuss preliminary work on learning a forward model of the dynamics of a balancing robot exploring simple arm movements. This model is then used to construct whole-body control strategies for regulating state variables using arm motion.

This paper describes a system called SITAC based on our proposed approach to discover concepts (called SITACs) in text archives that are identical semantically but alter their names over time. Our approach integrates natural language processing, association rule mining and contextual similarity to discover SITACs in order to answer historical queries over text corpora.

Many real-world applications, such as Supply Chain Management (SCM), can be modeled using multi-agent systems. One shortcoming of current SCM models is that their trust models are ad hoc and do not have a strong theoretical basis. We propose a trust model for SCM that is grounded in probabilistic game theory. In this model, trust can be gained through direct interactions, and/or by asking for information from other trustworthy agents. We will use this model to simulate and study supply chain market behavior.

The ability to form object categories is an important milestone in human infant development (Cohen 2003). We propose a framework that allows a robot to form a unified object categorization from several interactions with objects. This framework is consistent with the principle that robot a) Drop Block b) Grasp c) Move learning should be ultimately grounded in the robot's perceptual and behavioral repertoire (Stoytchev 2009). This paper builds upon our previous work (Griffith et al. 2009) by adding more exploratory behaviors (now 6 instead of 1) and by employing consensus clustering for finding a single, unified object categorization. The framework was tested on a container/non-container categorization task with 20 objects.

We propose a framework where humans and computers can collaborate seamlessly to solve problems. We do so by developing and applying a network model, namely Memenets, where human knowledge and reasoning are combined with machine computation to achieve problem-solving. The development of a Memenet is done in three steps: first, we simulate a machine-only network, as previous results have shown that memenets are efficient problem-solvers. Then, we perform an experiment with human agents organized in a online network. This allows us to investigate human behavior while solving problems in a social network and to postulate principles of agent communication in Memenets. These postulates describe an initial theory of how human-computer interaction functions inside social networks. In the third stage, postulates of step two allow one to combine human and machine computation to propose an integrated Memenet-based problem-solving computing model.

The UMLS Metathesaurus was assembled by integrating its ancestors. We introduced an inconsistency definition for some 150 source vocabularies; it contains more than Metathesaurus concepts based on their hierarchical relations 2 million concepts (i.e., clusters of synonymous terms coming and compute all such inconsistent concepts. After that we from multiple source vocabularies identified by a Concept manually review some of the inconsistent concepts to determine Unique Identifier). The UMLS Metathesaurus contains the ones that have erroneous synonymy relations such also more than 36 million relations between these concepts, as wrong synonymy.

Embedded systems consisting of collaborating agents capable of interacting with their environment are becoming ubiquitous. It is crucial for these systems to be able to adapt to the dynamic and uncertain characteristics of an open environment. In this paper, we argue that multiagent meta-level control (MMLC) 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 describe a reinforcement learning based approach to learn decentralized meta-control policies offline. We then propose to use the learned reward model as input to a global optimization algorithm to avoid conflicting meta-level decisions between coordinating agents. Our initial experiments in the context of NetRads, a multiagent tornado tracking application show that MMLC significantly improves performance in a 3-agent network.

As a robot formation increases in size or explores places where it is difficult for a human operator to interact, autonomous control becomes critical. We propose a distributed autonomous method for evaluating properties of multi-robot systems, and then discuss how this information can be applied to improve performance with respect to a given operation. We present this as an extension of our previous work on robot formations; however, the techniques described could be adapted to other multi-robot systems.