Learning the Semantics of Manipulation Action

In this paper we present a formal computational framework for modeling manipulation actions. The introduced formalism leads to semantics of manipulation action and has applications to both observing and understanding human manipulation actions as well as executing them with a robotic mechanism (e.g. a humanoid robot). It is based on a Combinatory Categorial Grammar. The goal of the introduced framework is to: (1) represent manipulation actions with both syntax and semantic parts, where the semantic part employs $\lambda$-calculus; (2) enable a probabilistic semantic parsing schema to learn the $\lambda$-calculus representation of manipulation action from an annotated action corpus of videos; (3) use (1) and (2) to develop a system that visually observes manipulation actions and understands their meaning while it can reason beyond observations using propositional logic and axiom schemata. The experiments conducted on a public available large manipulation action dataset validate the theoretical framework and our implementation

An Answer Set Programming-based Implementation of Epistemic Probabilistic Event Calculus

We describe a general procedure for translating Epistemic Probabilistic Event Calculus (EPEC) action language domains into Answer Set Programs (ASP), and show how the Python-driven features of the ASP solver Clingo can be used to provide efficient computation in this probabilistic setting. EPEC supports probabilistic, epistemic reasoning in domains containing narratives that include both an agent’s own action executions and environmentally triggered events. Some of the agent’s actions may be belief-conditioned, and some may be imperfect sensing actions that alter the strengths of previously held beliefs. We show that our ASP implementation can be used to provide query answers that fully correspond to EPEC’s own declarative, Bayesian-inspired semantics

Model checking probabilistic and stochastic extensions of the pi-calculus

We present an implementation of model checking for probabilistic and stochastic extensions of the pi-calculus, a process algebra which supports modelling of concurrency and mobility. Formal verification techniques for such extensions have clear applications in several domains, including mobile ad-hoc network protocols, probabilistic security protocols and biological pathways. Despite this, no implementation of automated verification exists. Building upon the pi-calculus model checker MMC, we first show an automated procedure for constructing the underlying semantic model of a probabilistic or stochastic pi-calculus process. This can then be verified using existing probabilistic model checkers such as PRISM. Secondly, we demonstrate how for processes of a specific structure a more efficient, compositional approach is applicable, which uses our extension of MMC on each parallel component of the system and then translates the results into a high-level modular description for the PRISM tool. The feasibility of our techniques is demonstrated through a number of case studies from the pi-calculus literature

Learning Effect Axioms via Probabilistic Logic Programming

In this paper we showed how we can automatically learn the structure and parameters of probabilistic effect axioms for the Simple Event Calculus (SEC) from positive and negative example interpretations stated as short dialogue sequences in natural language. We used the cplint framework for this task that provides libraries for structure and parameter learning and for answering queries with exact and inexact inference. The example dialogues that are used for learning the structure of the probabilistic logic program are parsed into dependency structures and then further translated into the Event Calculus notation with the help of a simple ontology. The novelty of our approach is that we can not only process uncertainty in event recognition but also learn the structure of effect axioms and combine these two sources of uncertainty to successfully answer queries under this probabilistic setting. Interestingly, our extension of the logic-based version of the SEC is completely elaboration-tolerant in the sense that the probabilistic version fully includes the logic-based version. This makes it possible to use the probabilistic version of the SEC in the traditional way as well as when we have to deal with uncertainty in the observed world. In the future, we would like to extend the probabilistic version of the SEC to deal -- among other things -- with concurrent actions and continuous change

Discrete Time Generative-Reactive Probabilistic Processes with Different Advancing Speeds

We present a process algebra expressing probabilistic external/internal choices, multi-way synchronizations, and processes with different advancing speeds in the context of discrete time, i.e. where time is not continuous but is represented by a sequence of discrete steps as in discrete time Markov chains (DTMCs). To this end, we introduce a variant of CSP that employs a probabilistic asynchronous parallel operator whose synchronization mechanism is based on a mixture of the classical generative and reactive models of probability. In particular, differently from existing discrete time process algebras, where parallel processes are executed in synchronous locksteps, the parallel operator that we adopt allows processes with different probabilistic advancing speeds (mean number of actions executed per time unit) to be modeled. Moreover, our generative-reactive synchronization mechanism makes it possible to always derive DTMCs in the case of fully specified systems. We then present a sound and complete axiomatization of probabilistic bisimulation over finite processes of our calculus, that is a smooth extension of the axiom system for a standard process algebra, thus solving the open problem of cleanly axiomatizing action restriction in the generative model. As a further result, we show that, when evaluating steady state based performance measures which are expressible by attaching rewards to actions, our approach provides an exact solution even if the advancing speeds are considered not to be probabilistic, without incurring the state space explosion problem that arises with standard synchronous approaches. We finally present a case study on multi-path routing showing the expressiveness of our calculus and that it makes it particularly easy to produce scalable specifications