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.

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.

One of the challenges of multiagent decision making is that the behavior needed to maximize utility can depend on what other agents choose to do: sometimes there is no "right" answer in the absence of knowledge of how opponents will act. The Nash equilibrium is a sensible choice of behavior because it represents a mutual best response. But, even when there is a unique equilibrium, other players are under no obligation to take part in it. This observation has been forcefully illustrated in the behavioral economics community where repeated experiments have shown individuals playing Nash equilibria and performing badly as a result. In this paper, we show how to apply a tool from behavioral economics called the Cognitive Hierarchy (CH) to the design of agents in general sum games. We attack the recently introduced ``Lemonade Game'' and show how the results of an open competition are well explained by CH. We believe this game, and perhaps many other similar games, boils down to predicting how deeply other agents in the game will be reasoning. An agent that does not reason enough risks being exploited by its opponents, while an agent that reasons too much may not be able to interact productively with its opponents. We demonstrate these ideas by presenting empirical results using agents from the competition and idealizations arising from a CH analysis.

In this paper we apply a Monte-Carlo Tree Search implementation that is boosted with domain knowledge to the game of poker. More specifically, we integrate an opponent model in the Monte-Carlo Tree Search algorithm to produce a strong poker playing program. Opponent models allow the search algorithm to focus on relevant parts of the game-tree. We use an opponent modelling approach that starts from a (learned) prior, i.e., general expectations about opponent behavior, and then learns a relational regression tree-function that adapts these priors to specific opponents. Our modelling approach can generate detailed game features or relations on-the-fly. Additionally, using a prior we can already make reasonable predictions even when limited experience is available for a particular player. We show that Monte-Carlo Tree Search with integrated opponent models performs well against state-of-the-art poker programs.

This paper examines the problem of finding articles in Wikipedia to match noun synsets in WordNet. The motivation is that these articles enrich the synsets with much more information than is already present in WordNet. Two methods are used. The first is title matching, following redirects and disambiguation links. The second is information retrieval over the set of articles. The methods are evaluated over a random sample set of 200 noun synsets which were manually annotated. With 10 candidate articles retrieved for each noun synset, the methods achieve recall of 93%. The manually annotated data set and the automatically generated candidate article sets are available online for research purposes.

We have been exploring the use of Web-derived knowledge bases through the development of Wikitology — a hybrid knowledge base of structured and unstructured information extracted from Wikipedia augmented by RDF data from DBpedia and other Linked Open Data resources. In this paper, we describe approaches that aid in enriching Wikipedia and thus the resources that derive from Wikipedia such as the Wikitology knowledge base, DBpedia, Freebase and Powerset.

Until recently, the AI and in particular the NLP community GA, immediately preceding the Twenty-Fourth AAAI Conference have relied on resources built manually by experts in on Artificial Intelligence -- AAAI 2010. It is a successor specific areas (in particular linguists, philosophers, cognitive to the workshops organized at AAAI 2008 entitled linguists). User contributed knowledge has opened up "Wikipedia and Artificial Intelligence: An Evolving Synergy" a new perspective, in that it captures the kind of knowledge (WikiAI 08) and at IJCAI 2009 entitled "User Contributed and organization that arises naturally out of the consensus Knowledge and Artificial Intelligence: An Evolving of the masses, and as such represents better our collective Synergy" (WikiAI 09). The outcome is a multifaceted and extremely This volume contains papers accepted for presentation at rich source of information, revealed through embedded annotations the workshop. We issued calls for regular papers, short latebreaking and structural information.

Solving real-world problems using symbolic planning often requires a simplified formulation of the original problem, since certain subproblems cannot be represented at all or only in a way leading to inefficiency. For example, manipulation planning may appear as a subproblem in a robotic planning context or a packing problem can be part of a logistics task. In this paper we propose an extension of PDDL for specifying semantic attachments. This allows the evaluation of grounded predicates, the change of fluents and the calculation of durations by externally specified functions. Furthermore, we describe a general schema of integrating semantic attachments into forward-chaining planning systems and report on our experience of adding this extension to the planner Temporal Fast Downward. Finally, we present some preliminary experiments using semantic attachments.

The Tekkotsu "crew" is a collection of interacting software components designed to relieve a programmer of much of the burden of specifying low-level robot behaviors. Using this abstract approach to robot programming we can teach beginning roboticists to develop interesting robot applications with relatively little effort.