We provide theoretical and empirical evidence for a type of asymmetry between causes and effects that is present when these are related via linear models contaminated with additive non-Gaussian noise. Assuming that the causes and the effects have the same distribution, we show that the distribution of the residuals of a linear fit in the anti-causal direction is closer to a Gaussian than the di...

Planning as heuristic search is a powerful approach to solving domain independent planning problems. In recent years, various successful heuristics and planners like FF, LPG, FAST DOWNWARD or SGPLAN have been proposed in this context. However, as heuristics only estimate the distance to goal states, a general problem of heuristic search is the existence of plateaus in the search space topology ...

Directed model checking is a well-established technique to tackle the state explosion problem when the aim is to find error states in large systems. In this approach, the state space traversal is guided through a function that estimates the distance to nearest error states. States with lower estimates are preferably expanded during the search. Obviously, the challenge is to develop distance fun...

We pose a multi-model system parameter estimation problem. A multi-model system is a linearly parameterized system H(z, p) = ∑np i=1 piHi(z). The parameter estimation problem is: given the set of systems {Hi(z)} np i=1, describing the multi-model system, find a causal system that assumes as an input the input/output signals of the multi-model system and produces as an output the parameter estim...

This paper deals with problems of recovering a causal structure by using not only conditional independence relationships but also prior knowledge when data are generated according to the causal structure among variables. Although some algorithms for recovering a causal structure based on independencies have been developed, the influence of prior knowledge on the recovery algorithms has not been...

We consider a generalization of the PDB homomorphism abstractions to what is called “structural patterns”. The basic idea is in abstracting the problem in hand into provably tractable fragments of optimal planning, alleviating by that the constraint of PDBs to use projections of only low dimensionality. We introduce a general framework for additive structural patterns based on decomposing the p...

We present a new approach to token-level causal reasoning that we call Sequences Of Mechanisms (SoMs), which models causality as a dynamic sequence of active mechanisms that chain together to propagate causal influence through time. We motivate this approach by using examples from AI and robotics and show why existing approaches are inadequate. We present an algorithm for causal reasoning based...

Abstract. Efficient algorithms exist for fault detection and isolation of physical systems based on functional redundancy. In a qualitative approach, this redundancy can be captured by a temporal causal graph (TCG), a directed graph that may include temporal information. However, in a detailed continuous model, time constants may be present that are beyond the bandwidth of the data acquisition ...

The complexity of existing planners is bounded by the length of the resulting plan, a fact that limits planning to domains with relatively short solutions. We present a novel planning algorithm that uses the causal graph of a domain to decompose it into subproblems and stores subproblem plans in memory as macros. In many domains, the resulting plan can be expressed using relatively few macros, ...

In our previous work (e.g., 4], 5], 6], 7]), we have formalized the story understanding process based on scripts and plans with stepwise default theories. While those theories ooer nal results for understanding a speciic story, they do not provide the history of changes of partial states of any objects the story may concern. Moreover, the causal models for missing events are incomplete in scrip...