Learning probabilistic relational planning rules

To learn to behave in highly complex domains, agents must represent and learn compact models of the world dynamics. In this paper, we present an algorithm for learning probabilistic STRIPS-like planning operators from examples. We demonstrate the effective learning of rule-based operators for a wide range of traditional planning domains

Gallium Nitrate Is Efficacious in Murine Models of Tuberculosis and Inhibits Key Bacterial Fe-Dependent Enzymes

Acquiring iron (Fe) is critical to the metabolism and growth of Mycobacterium tuberculosis. Disruption of Fe metabolism is a potential approach for novel antituberculous therapy. Gallium (Ga) has many similarities to Fe. Biological systems are often unable to distinguish Ga3+ from Fe3+. Unlike Fe3+, Ga3+ cannot be physiologically reduced to Ga2+. Thus, substituting Ga for Fe in the active site of enzymes may render them nonfunctional. We previously showed that Ga inhibits growth of M. tuberculosis in broth and within cultured human macrophages. We now report that Ga(NO3)3 shows efficacy in murine tuberculosis models. BALB/c SCID mice were infected intratracheally with M. tuberculosis, following which they received daily intraperitoneal saline, Ga(NO3)3, or NaNO3. All mice receiving saline or NaNO3 died. All Ga(NO3)3-treated mice survived. M. tuberculosis CFU in the lungs, liver, and spleen of the NaNO3-treated or saline-treated mice were significantly higher than those in Ga-treated mice. When BALB/c mice were substituted for BALB/c SCID mice as a chronic (nonlethal) infection model, Ga(NO3)3 treatment significantly decreased lung CFU. To assess the mechanism(s) whereby Ga inhibits bacterial growth, the effect of Ga on M. tuberculosis ribonucleotide reductase (RR) (a key enzyme in DNA replication) and aconitase activities was assessed. Ga decreased M. tuberculosis RR activity by 50 to 60%, but no additional decrease in RR activity was seen at Ga concentrations that completely inhibited mycobacterial growth. Ga decreased aconitase activity by 90%. Ga(NO3)3 shows efficacy in murine M. tuberculosis infection and leads to a decrease in activity of Fe-dependent enzymes. Additional work is warranted to further define Ga’s mechanism of action and to optimize delivery forms for possible therapeutic uses in humans

Logical particle filtering

Abstract. In this paper, we consider the problem of filtering in relational hidden Markov models. We present a compact representation for such models and an associated logical particle filtering algorithm. Each particle contains a logical formula that describes a set of states. The algorithm updates the formulae as new observations are received. Since a single particle tracks many states, this filter can be more accurate than a traditional particle filter in high dimensional state spaces, as we demonstrate in experiments. Consider an agent operating in a complex environment, made up of an unknown, possibly infinite, number of objects. The agent can take actions and make observations of the state of the world, and it knows a probabilistic model of how the state changes over time as a result of its actions and of how the observations are generated from the states. How can it efficiently estimate the underlying state of the environment? Filtering is the problem of predicting a distribution over the underlying environment state given a history of the agent’

Kaelbling. Learning symbolic models of stochastic domains

In this article, we work towards the goal of developing agents that can learn to act in complex worlds. We develop a probabilistic, relational planning rule representation that compactly models noisy, nondeterministic action effects, and show how such rules can be effectively learned. Through experiments in simple planning domains and a 3D simulated blocks world with realistic physics, we demonstrate that this learning algorithm allows agents to effectively model world dynamics. 1