instruction:
In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
question:
We investigate the use of temporally abstract actions, or macro-actions, in the solution of Markov decision processes. Unlike current mod­ els that combine both primitive actions and macro-actions and leave the state space un­ changed, we propose a hierarchical model (using an abstract MDP) that works with macro-actions only, and that significantly reduces the size of the state space. This is achieved by treating macro­ actions as local policies that act in certain regions of state space, and by restricting states in the ab­ stract MDP to those at the boundaries of regions. The abstract MDP approximates the original and can be solved more efficiently. We discuss sev­ eral ways in which macro-actions can be gen­ erated to ensure good solution quality. Finally, we consider ways in which macro-actions can be reused to solve multiple, related MDPs; and we show that this can justify the computational over­ head of macro-action generation.
answer:
Hierarchical Solution of Markov Decision Processes using Macro-actions


question:
Multimodal sentiment analysis is an increasingly popular research area, which extends the conventional language-based definition of sentiment analysis to a multimodal setup where other relevant modalities accompany language. In this paper, we pose the problem of multimodal sentiment analysis as modeling intra-modality and inter-modality dynamics. We introduce a novel model, termed Tensor Fusion Network, which learns both such dynamics end-to-end. The proposed approach is tailored for the volatile nature of spoken language in online videos as well as accompanying gestures and voice. In the experiments, our model outperforms state-ofthe-art approaches for both multimodal and unimodal sentiment analysis.
answer:
Tensor Fusion Network for Multimodal Sentiment Analysis


question:
This paper describes the best first search strategy used by U-Plan (Mansell 1993a), a planning system that constructs quantitatively ranked plans given an incomplete description of an uncertain environment. U-Plan uses uncertain and incomplete evidence describing the environment, characterises it using a Dempster­ Shafer interval, and generates a set of possible world states. Plan construction takes place in an abstraction hierarchy where strategic decisions are made before tactical decisions. Search through this abstraction hierarchy is guided by a quantitative measure (expected fulfilment) based on decision theory. The search strategy is best first with the provision to update expected fulfilments and review previous decisions in the light of planning developments. U-Pian generates multiple plans for multiple possible worlds, and attempts to use existing plans for new world situations. A super-plan is then constructed, based on merging the set of plans and appropriately timed knowledge acquisition operators, which are used to decide between plan alternatives during plan execution.
answer:

Operator Selection While Planning Under Uncertainty