Cognitive systems with human-level intelligence must display a wide range of abilities, including reasoning about the beliefs of others, hypothetical and future situations, quantifiers, probabilities, and counterfactuals. While each of these deals in some way with reasoning about alternative states of reality, no single knowledge representation framework deals with them in a unified and scalable manner. As a consequence it is difficult to build cognitive systems for domains that require each of these abilities to be used together. To enable this integration we propose a representational framework based on synchronizing beliefs between worlds. Using this framework, each of these tasks can be reformulated into a reasoning problem involving worlds. This demonstrates that the notions of worlds and inheritance can bring significant parsimony and broad new abilities to knowledge representation.
A recent trend in planning with incomplete information is to model the actions of a planning problem as nondeterministic transitions over the belief states of a planner, and to search for a plan that terminates in a desired goal state no matter how these transitions turn out. We show that this view of planning is fundamentally limited. Any plan that is successful by this criteria has an upper bound on the number of actions it can execute. Specifically, the account will not work when iterative plans are needed. We also show that by modifying the definition slightly, we obtain another account of planning that does work properly even for iterative plans. Although the argument is presented in an abstract form, we illustrate the issues using a simple concrete example.
Clustering uncertain data is an essential task in data mining for the internet of things. Possible world based algorithms seem promising for clustering uncertain data. However, there are two issues in existing possible world based algorithms: (1) They rely on all the possible worlds and treat them equally, but some marginal possible worlds may cause negative effects. (2) They do not well utilize the consistency among possible worlds, since they conduct clustering or construct the affinity matrix on each possible world independently. In this paper, we propose a representative possible world based consistent clustering (RPC) algorithm for uncertain data. First, by introducing representative loss and using Jensen-Shannon divergence as the distribution measure, we design a heuristic strategy for the selection of representative possible worlds, thus avoiding the negative effects caused by marginal possible worlds. Second, we integrate a consistency learning procedure into spectral clustering to deal with the representative possible worlds synergistically, thus utilizing the consistency to achieve better performance. Experimental results show that our proposed algorithm performs better than the state-of-the-art algorithms.
April 28, 2017 --Science fiction has long painted space settlements as inevitable, and talk of Martian brick-building and life-supporting gardens makes it feel closer than ever. But some suggest a simpler path to long-term living in space: orbital habitats near Earth. SpaceX CEO Elon Musk placed colonization under serious consideration last fall at the International Astronautical Congress in Guadalajara, Mexico, when he announced his intention to bring 1 million people to Mars. But while the presentation was heavy on rocket technicalities, it left out details of how colonists will survive, much less raise children in a high-radiation, low-gravity environment millions of miles away. NASA contractor and colonization advocate Al Globus says there's a "radically easier" way: large, round habitats known as O'Neill cylinders that orbit nearby, spinning at just the right speed to create the sensation of normal gravity inside.
Probabilistic Roadmaps (PRM) are a commonly used class of algorithms for robot navigation tasks where obstacles are present in the environment. We examine the situation where the obstacle positions are not precisely known. A subset of the edges in the PRM graph may possibly intersect the obstacles, and as the robot traverses the graph it can make noisy observations of these uncertain edges to determine if it can traverse them or not. The problem is to traverse the graph from an initial vertex to a goal without taking a blocked edge, and to do this optimally the robot needs to consider the observations it can make as well as the structure of the graph. In this paper we show how this problem can be represented as a POMDP. We show that while too large to be solved with exact methods, approximate point based methods can provide a good quality solution. While feasible for smaller examples, this approach isn't scalable. By exploiting the structure in the belief space, we can construct an approximate belief-space MDP that can be solved efficiently using recent techniques in MDP planning. We then demonstrate that this gives near optimal results in most cases while achieving an order of magnitude speed-up in policy generation time.