We consider in this paper the size of a Decomposable Negation Normal Form (DNNF) that respects a given vtree (known as structured DNNF). This representation of propositional knowledge bases was introduced recently and shown to include OBDD as a special case (an OBDD variable ordering is a special type of vtree). We provide a lower bound on the size of any structured DNNF and discuss three particular instances of this bound, which correspond to three distinct subsets of structured DNNF (including OBDD). We show that our lower bound subsumes the influential Sieling and Wegener’s lower bound for OBDDs, which is typically used for identifying variable orderings that will cause a blowup in the OBDD size. We show that our lower bound allows for similar usage but with respect to vtrees, which provide structure for DNNFs in the same way that variable orderings provide structure for OBDDs. We finally discuss some of the theoretical and practical implications of our lower bound.

In computational social choice, one important problem is to take the votes of a subelectorate (subset of the voters), and summarize them using a small number of bits. This needs to be done in such a way that, if all that we know is the summary, as well as the votes of voters outside the subelectorate, we can conclude which of the m alternatives wins. This corresponds to the notion of compilation complexity, the minimum number of bits required to summarize the votes for a particular rule, which was introduced by Chevaleyre et al. [IJCAI-09]. We study three different types of compilation complexity. The first, studied by Chevaleyre et al., depends on the size of the subelectorate but not on the size of the complement (the voters outside the subelectorate). The second depends on the size of the complement but not on the size of the subelectorate. The third depends on both. We first investigate the relations among the three types of compilation complexity. Then, we give upper and lower bounds on all three types of compilation complexity for the most prominent voting rules. We show that for l -approval (when l ≤ m /2), Borda, and Bucklin, the bounds for all three types are asymptotically tight, up to a multiplicative constant; for l-approval (when l > m /2), plurality with runoff, all Condorcet consistent rules that are based on unweighted majority graphs (including Copeland and voting trees), and all Condorcet consistent rules that are based on the order of pairwise elections (including ranked pairs and maximin), the bounds for all three types are asymptotically tight up to a multiplicative constant when the sizes of the subelectorate and its complement are both larger than m 1+ε for some ε > 0.

This paper investigates search techniques for multi-agent settings in which the most suitable agent, according to given criteria, needs to be found. In particular, it considers the case where the searching agent incurs a cost for learning the value of an agent and the goal is to minimize the expected overall cost of search by iteratively increasing the extent of search. This kind of search is applicable to various domains, including auctions, first responders, and sensor networks. Using an innovative transformation of the extents-based sequence to a probability-based one, the optimal sequence is proved to consist of either a single search iteration or an infinite sequence of increasing search extents. This leads to a simplified characterization of the the optimal search sequence from which it can be derived. This method is also highly useful for legacy economic-search applications, where all agents are considered suitable candidates and the goal is to optimize the search process as a whole. The effectiveness of the method for both best-valued search and economic search is demonstrated numerically using a synthetic environment.

We consider how to learn Hierarchical Task Networks (HTNs) for planning problems in which both the quality of solution plans generated by the HTNs and the speed at which those plans are found is important. We describe an integration of HTN Learning with Reinforcement Learning to both learn methods by analyzing semantic annotations on tasks and to produce estimates of the expected values of the learned methods by performing Monte Carlo updates. We performed an experiment in which plan quality was inversely related to plan length. In two planning domains, we evaluated the planning performance of the learned methods in comparison to two state-of-the-art satisficing classical planners, FastForward and SGPlan6, and one optimal planner, HSP*. The results demonstrate that a greedy HTN planner using the learned methods was able to generate higher quality solutions than SGPlan6 in both domains and FastForward in one. Our planner, FastForward, and SGPlan6 ran in similar time, while HSP* was exponentially slower.

The field of multi-robot coordination, specifically robot formation control, is rapidly expanding, with many applications proposed. In our previous work, we considered the problem of establishing and maintaining a formation of robots given an already connected network. We now propose a distributed auction-based method to autonomously initialize and reorganize the network structure of a formation of robots.

We present an algorithm that exploits the complimentary benefits of best-first search (BFS) and depth-first search (DFS) by performing limited DFS lookaheads from the frontier of BFS. We show that this continuum requires significantly less memory than BFS. In addition, a time speedup is also achieved when choosing the lookahead depth correctly. We demonstrate this idea for breadth-first search and for A*. Additionally, we show that when using inconsistent heuristics, Bidirectional Pathmax (BPMX), can be implemented very easily on top of the lookahead phase. Experimental results on several domains demonstrate the benefits of all our ideas.

We present an instance-based, online method for learning action models in unanticipated, relational domains. Our algorithm memorizes pre- and post-states of transitions an agent encounters while experiencing the environment, and makes predictions by using analogy to map the recorded transitions to novel situations. Our algorithm is implemented in the Soar cognitive architecture, integrating its task-independent episodic memory module and analogical reasoning implemented in procedural memory. We evaluate this algorithm’s prediction performance in a modified version of the blocks world domain and the taxi domain. We also present a reinforcement learning agent that uses our model learning algorithm to significantly speed up its convergence to an optimal policy in the modified blocks world domain.

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.

Good and generous knowledge sources, reliable and efficient induction patterns, and automatic and controllable quality assertion approaches are three critical issues to commonsense knowledge (CSK) acquisition. This paper employs Open Mind Common Sense (OMCS), a volunteers-contributed CSK database, to study the first and the third issues. For those stylized CSK, our result shows that over 40% of CSK for four predicate types in OMCS can be found in the web, which contradicts to the assumption that CSK is not communicated in texts. Moreover, we propose a commonsense knowledge classifier trained from OMCS, and achieve high precision in some predicate types, e.g., 82.6% in HasProperty. The promising results suggest new ways of analyzing and utilizing volunteer-contributed knowledge to design systems automatically mining commonsense knowledge from the web.

Research on information extraction (IE) seeks to distill relational tuples from natural language text, such as the contents of the WWW. Most IE work has focussed on identifying static facts, encoding them as binary relations. This is unfortunate, because the vast majority of facts are fluents, only holding true during an interval of time. It is less helpful to extract PresidentOf(Bill-Clinton, USA) without the temporal scope 1/20/93 — 1/20/01. This paper presents TIE, a novel, information-extraction system, which distills facts from text while inducing as much temporal information as possible. In addition to recognizing temporal relations between times and events, TIE performs global inference, enforcing transitivity to bound the start and ending times for each event. We introduce the notion of temporal entropy as a way to evaluate the performance of temporal IE systems and present experiments showing that TIE outperforms three alternative approaches.