In this paper, we extend the persona-based sequence-to-sequence (Seq2Seq) neural network conversation model to multi-turn dialogue by modifying the state-of-the-art hredGAN architecture. To achieve this, we introduce an additional input modality into the encoder and decoder of hredGAN to capture other attributes such as speaker identity, location, sub-topics, and other external attributes that might be available from the corpus of human-to-human interactions. The resulting persona hredGAN (phredGAN) shows better performance than both the existing persona-based Seq2Seq and hredGAN models when those external attributes are available in a multi-turn dialogue corpus. This superiority is demonstrated on TV drama series with character consistency (such as Big Bang Theory and Friends) and customer service interaction datasets such as Ubuntu dialogue corpus in terms of perplexity, BLEU, ROUGE, and Distinct n-gram scores.

In order to be helpful to people, the intelligent interfaces of the future will have to acquire, represent, and infer simple knowledge about everyday life and activities. While much work in AI has represented this knowledge at the word, sentence, and logical assertion level, we see a growing need to understand it at a larger granularity, that of stories.

We present an implemented method for encoding reasoning problems of a discrete version of the classical logic event calculus in propositional conjunctive normal form, enabling the problems to be solved efficiently by off-the-shelf complete satisfiability (SAT) solvers. We build on the previous encoding method of Shanahan and Witkowski, extending it to support causal constraints, concurrent events, determining fluents, effect axioms with conditions, events triggered by conditions, gradual change, incompletely specified initial situations, state constraints, and release from the commonsense law of inertia. We present an alternative classical logic axiomatization of the event calculus and prove its equivalence to a standard axiomatization for integer time. We describe our encoding method based on the alternative axiomatization and prove its correctness. We evaluate the method on 14 benchmark reasoning problems for the event calculus and compare performance with the causal calculator on eight problems in the zoo world domain.

Digital devices today have little understanding of their real-world context, and as a result they often make stupid mistakes. To improve this situation we are developing a database of world knowledge called ThoughtTreasure at the same time that we develop intelligent applications. In this paper we present one such application, SensiCal, a calendar with a degree of common sense. We discuss the pieces of common sense important in calendar management and present methods for extracting relevant information from calendar items.

Atomic transactions are useful in distributed systems as a means of providing reliable operation in the face of hardware failures. Nested transactions are a generalization of traditional transactions in which transactions may be composed of other transactions. The programmer may initiate several transactions from within a transaction, and serializability of the transactions is guaranteed even if they are executed concurrently. In addition, transactions invoked from within a given transaction fail independently of their invoking transaction and of one another, allowing use of alternate transactions to accomplish the desired task in the event that the original should fail. Thus nested transactions are the basis for a general-purpose reliable programming environment in which transactions are modules which may be composed freely.