TL;DR: The syntax and modelling style of PDDL+, a planning domain description language for modelling mixed discrete-continuous planning domains, is described, showing that the language makes convenient the modelling of complex time-dependent effects.
Abstract: In this paper we present PDDL+, a planning domain description language for modelling mixed discrete-continuous planning domains. We describe the syntax and modelling style of PDDL+, showing that the language makes convenient the modelling of complex time-dependent effects. We provide a formal semantics for PDDL+ by mapping planning instances into constructs of hybrid automata. Using the syntax of HAs as our semantic model we construct a semantic mapping to labelled transition systems to complete the formal interpretation of PDDL+ planning instances.
An advantage of building a mapping from PDDL+ to HA theory is that it forms a bridge between the Planning and Real Time Systems research communities. One consequence is that we can expect to make use of some of the theoretical properties of HAs. For example, for a restricted class of HAs the Reachability problem (which is equivalent to Plan Existence) is decidable.
PDDL+ provides an alternative to the continuous durative action model of PDDL2.1, adding a more flexible and robust model of time-dependent behaviour.
TL;DR: This paper develops an algorithm called ARMS (action-relation modelling system) for automatically discovering action models from a set of successful observed plans, and lays the theoretical foundations of the learning problem and evaluates the effectiveness of ARMS empirically.
TL;DR: Evidence is provided that specialized (sports) motor experience enhances action-related language understanding by recruitment of left dorsal lateral premotor cortex, a region normally devoted to higher-level action selection and implementation—even when there is no intention to perform a real action.
Abstract: Experience alters behavior by producing enduring changes in the neural processes that support performance. For example, performing a specific action improves the execution of that action via changes in associated sensory and motor neural circuitry, and experience using language improves language comprehension by altering the anatomy and physiology of perisylvian neocortical brain regions. Here we provide evidence that specialized (sports) motor experience enhances action-related language understanding by recruitment of left dorsal lateral premotor cortex, a region normally devoted to higher-level action selection and implementation-even when there is no intention to perform a real action. Experience playing and watching sports has enduring effects on language understanding by changing the neural networks that subserve comprehension to incorporate areas active in performing sports skills. Without such experience, sport novices recruit lower-level sensory-motor regions, thought to support the instantiation of movement, during language processing, and activity in primary motor areas does not help comprehension. Thus, the language system is sufficiently plastic and dynamic to encompass expertise-related neural recruitment outside core language networks.
TL;DR: Recent progress in these areas may lead to the creation of planners which are based on the ideas of logic programming and combine the use of expressive action description languages with efficient computational procedures.
Abstract: This is a discussion of some of the achievements and challenges related to representing actions and the design of planners from the perspective of logic programming. We talk about recent work on action languages and translating them into logic programming, on representing possible histories of an action domain by answer sets, on efficient implementations of the answer set semantics and their use for generating plans, and on causal logic and its relation to planning algorithms. Recent progress in these areas may lead to the creation of planners which are based on the ideas of logic programming and combine the use of expressive action description languages with efficient computational procedures.
TL;DR: It is concluded that sensori-motor cortices are activated during a variety of language comprehension tasks, for both concrete and abstract language, and should be incorporated into models of the neurocognitive architecture of language.
Abstract: Do people use sensori-motor cortices to understand language? Here we review neurocognitive studies of language comprehension in healthy adults and evaluate their possible contributions to theories of language in the brain. We start by sketching the minimal predictions that an embodied theory of language understanding makes for empirical research, and then survey studies that have been offered as evidence for embodied semantic representations. We explore four debated issues: first, does activation of sensori-motor cortices during action language understanding imply that action semantics relies on mirror neurons? Second, what is the evidence that activity in sensori-motor cortices plays a functional role in understanding language? Third, to what extent do responses in perceptual and motor areas depend on the linguistic and extra-linguistic context? And finally, can embodied theories accommodate language about abstract concepts? Based on the available evidence, we conclude that sensori-motor cortices are activated during a variety of language comprehension tasks, for both concrete and abstract language. Yet, this activity depends on the context in which perception and action words are encountered. Although modality-specific cortical activity is not a sine qua non of language processing even for language about perception and action, sensori-motor regions of the brain appear to make functional contributions to the construction of meaning, and should therefore be incorporated into models of the neurocognitive architecture of language.