This paper presents a meta-heuristic algorithm for solving the Flexible Job Shop Scheduling Problem (FJSSP). This strategy, known as Iterative Flattening Search (IFS), iteratively applies a relaxation-step, in which a subset of scheduling decisions are randomly retracted from the current solution; and a solving-step, in which a new solution is incrementally recomputed from this partial schedule. This work contributes two separate results: (1) it proposes a constraint-based procedure extending an existing approach previously used for classical Job Shop Scheduling Problem; (2) it proposes an original relaxation strategy on feasible FJSSP solutions based on the idea of randomly breaking the execution orders of the activities on the machines and opening the resource options for some activities selected at random. The efficacy of the overall heuristic optimization algorithm is demonstrated on a set of well-known benchmarks.

A* with admissible heuristics is a very successful approach to optimal planning. But how to derive such heuristics automatically? Merge-and-shrink abstraction (M&S) is a general approach to heuristic design whose key advantage is its capability to make very fine-grained choices in defining abstractions. However, little is known about how to actually make these choices. We address this via the well-known notion of bisimulation. When aggregating only bisimilar states, M&S yields a perfect heuristic. Alas, bisimulations are exponentially large even in trivial domains. We show how to apply label reduction — not distinguishing between certain groups of operators — without incurring any information loss, while potentially reducing bisimulation size exponentially. In several benchmark domains, the resulting algorithm computes perfect heuristics in polynomial time. Empirically, we show that approximating variants of this algorithm improve the state of the art in M&S heuristics. In particular, a simple hybrid of two such variants is competitive with the leading heuristic LM-cut.

The field of deterministic AI planning can roughly be divided into two approaches - classical state-based planning and hierarchical task network (HTN) planning. The plan existence problem of the former is known to be decidable while it has been proved undecidable for the latter. When extending HTN planning by allowing the unrestricted insertion of tasks and ordering constraints, one obtains a form of planning which is often referred to as "hybrid planning." We present a simplified formalization of HTN planning with and without task insertion. We show that the plan existence problem is undecidable for the HTN setting without task insertion and that it becomes decidable when allowing task insertion. In the course of the proof, we obtain an upper complexity bound of EXPSPACE for the plan existence problem for propositional HTN planning with task insertion.

Planning with partial observability can be formulated as a non-deterministic search problem in belief space. The problem is harder than classical planning as keeping track of beliefs is harder than keeping track of states, and searching for action policies is harder than searching for action sequences. In this work, we develop a framework for partial observability that avoids these limitations and leads to a planner that scales up to larger problems. For this, the class of problems is restricted to those in which 1) the non-unary clauses representing the uncertainty about the initial situation are nvariant, and 2) variables that are hidden in the initial situation do not appear in the body of conditional effects, which are all assumed to be deterministic. We show that such problems can be translated in linear time into equivalent fully observable non-deterministic planning problems, and that an slight extension of this translation renders the problem solvable by means of classical planners. The whole approach is sound and complete provided that in addition, the state-space is connected. Experiments are also reported.

This paper presents an algorithm for finding approximately optimal policies in very large Markov decision processes by constructing a hierarchical model and then solving it approximately. It exploits factored representations to achieve compactness and efficiency and to discover connectivity properties of the domain. We provide a bound on the quality of the solutions and give asymptotic analysis of the runtimes; in addition we demonstrate performance on a collection of very large domains. Results show that the quality of resulting policies is very good and the total running times, for both creating and solving the hierarchy, are significantly less than for an optimal factored MDP solver.

In this paper, to support more precise Chinese Out-of-Vocabulary (OOV) term detection and Part-of-Speech (POS) guessing, a unified mechanism is proposed and formulated based on the fusion of multiple features and supervised learning. Besides all the traditional features, the new features for statistical information and global contexts are introduced, as well as some constraints and heuristic rules, which reveal the relationships among OOV term candidates. Our experiments on the Chinese corpora from both People’s Daily and SIGHAN 2005 have achieved the consistent results, which are better than those acquired by pure rule-based or statistics-based models. From the experimental results for combining our model with Chinese monolingual retrieval on the data sets of TREC-9, it is found that the obvious improvement for the retrieval performance can also be obtained.

Metaphorical interpretation and affect detection using context profiles from open-ended text input are challenging in affective language processing field. In this paper, we explore recognition of a few typical affective metaphorical phenomena and context-based affect sensing using the modeling of speakers’ improvisational mood and other participants’ emotional influence to the speaking character under the improvisation of loose scenarios. The overall updated affect detection module is embedded in an AI agent. The new developments have enabled the AI agent to perform generally better in affect sensing tasks. The work emphasizes the conference themes on affective dialogue processing, human-agent interaction and intelligent user interfaces.

This paper is concerned with the use of conversational agents as an interaction paradigm for accessing open domain encyclopedic knowledge by means of Wikipedia. More precisely, we describe a dialogue-based question answering system for German which utilizes Wikipedia-based topic models as a reference point for context detection and answer prediction. We investigate two different per- spectives to the task of interfacing virtual agents with collaborative knowledge. First, we exploit the use of Wikipedia categories as a basis for identifying the broader topic of a spoken utterance. Second, we describe how to enhance the conversational behavior of the virtual agent by means of a Wikipedia-based question answering component which incorporates the question topic. At large, our approach identifies topic-related focus terms of a user’s question, which are subsequently mapped onto a category taxonomy. Thus, we utilize the taxonomy as a reference point to derive topic labels for a user’s question. The employed topic model is thereby based on explicitly given concepts as represented by the document and category structure of the Wikipedia knowledge base. Identified topic categories are subsequently combined with different linguistic filtering methods to improve answer candidate retrieval and reranking. Results show that the topic model approach contributes to an enhancement of the conversational behavior of virtual agents.

The paper describes a method of relation extraction, which is based on parsing the input text using a combination of a generic HPSG-based grammar and a highly focused domain- and relation-specific lexicon. We also show a method of unsupervised acquisition of such a lexicon from a large unlabeled corpus. Together, the methods introduce a novel approach to the “Open IE” task, which is superior in accuracy and in quality of relation identification to the existing approaches.

Designing dialog policies for voice-enabled interfaces is a tailoring job that is most often left to natural language processing experts. This job is generally redone for every new dialog task because cross-domain transfer is not possible. For this reason, machine learning methods for dialog policy optimization have been investigated during the last 15 years. Especially, reinforcement learning (RL) is now part of the state of the art in this domain. Standard RL methods require to test more or less random changes in the policy on users to assess them as improvements or degradations. This is called on policy learning. Nevertheless, it can result in system behaviors that are not acceptable by users. Learning algorithms should ideally infer an optimal strategy by observing interactions generated by a non-optimal but acceptable strategy, that is learning off-policy. In this contribution, a sample-efficient, online and off-policy reinforcement learning algorithm is proposed to learn an optimal policy from few hundreds of dialogues generated with a very simple handcrafted policy.