|
PDDL
The Planning Domain Definition Language (PDDL) is an attempt to standardize Automated planning and scheduling, Artificial Intelligence (AI) planning languages. It was first developed by Drew McDermott and his colleagues in 1998 (inspired by Stanford Research Institute Problem Solver, STRIPS and Action description language, ADL among others) mainly to make the 1998/200International Planning Competition (IPC)possible, and then evolved with each competition. The standardization provided by PDDL has the benefit of making research more reusable and easily comparable, though at the cost of some expressive power, compared to domain-specific systems. De facto official versions of PDDL PDDL1.2 This was the official language of the 1st an2ndIPC in 1998 and 2000 respectively. It separated the model of the planning problem in two major parts: (1) domain description and (2) the related problem description. Such a division of the model allows for an intuitive separation of those elements, wh ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
PPDDL
The Planning Domain Definition Language (PDDL) is an attempt to standardize Automated planning and scheduling, Artificial Intelligence (AI) planning languages. It was first developed by Drew McDermott and his colleagues in 1998 (inspired by Stanford Research Institute Problem Solver, STRIPS and Action description language, ADL among others) mainly to make the 1998/200International Planning Competition (IPC)possible, and then evolved with each competition. The standardization provided by PDDL has the benefit of making research more reusable and easily comparable, though at the cost of some expressive power, compared to domain-specific systems. De facto official versions of PDDL PDDL1.2 This was the official language of the 1st an2ndIPC in 1998 and 2000 respectively. It separated the model of the planning problem in two major parts: (1) domain description and (2) the related problem description. Such a division of the model allows for an intuitive separation of those elements, wh ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Preference-based Planning
In artificial intelligence, preference-based planning is a form of automated planning and scheduling which focuses on producing plans that additionally satisfy as many user-specified preferences as possible. In many problem domains, a task can be accomplished by various sequences of actions (also known as plans). These plans can vary in quality: there can be many ways to solve a problem but one generally prefers a way that is, e.g., cost-effective, quick and safe. Preference-based planners take these preferences into account when producing a plan for a given problem. Examples of preference-based planning software include ''PPLAN''PPLAN Bienvenu et al. and ''HTNPlan-P''HTN P ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Stanford Research Institute Problem Solver
The Stanford Research Institute Problem Solver, known by its acronym STRIPS, is an automated planner developed by Richard Fikes and Nils Nilsson in 1971 at SRI International. The same name was later used to refer to the formal language of the inputs to this planner. This language is the base for most of the languages for expressing automated planning problem instances in use today; such languages are commonly known as action languages. This article only describes the language, not the planner. Definition A STRIPS instance is composed of: * An initial state; * The specification of the goal states – situations which the planner is trying to reach; * A set of actions. For each action, the following are included: ** preconditions (what must be established before the action is performed); ** postconditions (what is established after the action is performed). Mathematically, a STRIPS instance is a quadruple \langle P,O,I,G \rangle, in which each component has the following meaning: ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Action Description Language
In artificial intelligence, action description language (ADL) is an automated planning and scheduling system in particular for robots. It is considered an advancement of STRIPS. Edwin Pednault (a specialist in the field of data abstraction and modelling who has been an IBM Research Staff Member in the Data Abstraction Research Group since 1996) proposed this language in 1987. It is an example of an action language. Origins Pednault observed that the expressive power of STRIPS was susceptible to being improved by allowing the effects of an operator to be conditional. This is the main idea of ADL-A, which is basically the propositional fragment of the ADL proposed by Pednault, with ADL-B an extension of -A. In the -B extension, actions can be described with indirect effects by the introduction of a new kind of propositions: ”static laws". A third variation of ADL is ADL-C which is similar to -B, in the sense that its propositions can be classified into static and dynamic laws, but ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Drew McDermott
Drew McDermott (December 27, 1949 – May 26, 2022) was a professor of Computer Science at Yale University. He was known for his contributions in artificial intelligence and planning. Education Drew McDermott earned Bachelor of Science, B.S., Master of Science, M.S., and Doctor of Philosophy, Ph.D. degrees from the Massachusetts Institute of Technology (MIT). He became a tenured full professor at Yale in 1983. He served as Chair of the Department from 1991 to 1995. He retired in 2018. Research His research has been in the area of artificial intelligence, with side excursions into philosophy. His Ph.D. dissertation was in the area of automated planning. In that work, he coined the term "task network" to refer to hierarchies of abstract and concrete actions and policies. He did seminal work in Non-monotonic logic in the early 1980s, and was an advocate for the "logicist" methodology in AI, defined as formalizing knowledge and reasoning in terms of deduction and quasideduction. In 1 ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Automated Planning And Scheduling
Automation describes a wide range of technologies that reduce human intervention in processes, namely by predetermining decision criteria, subprocess relationships, and related actions, as well as embodying those predeterminations in machines. Automation has been achieved by various means including Mechanical system, mechanical, hydraulic, pneumatic, electrical, electronic devices, and computers, usually in combination. Complicated systems, such as modern factories, airplanes, and ships typically use combinations of all of these techniques. The benefit of automation includes labor savings, reducing waste, savings in electricity costs, savings in material costs, and improvements to quality, accuracy, and precision. Automation includes the use of various equipment and control systems such as machinery, processes in factories, boilers, and heat-treating ovens, switching on telephone networks, steering, and stabilization of ships, aircraft, and other applications and vehicles with ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Lambda Calculus
Lambda calculus (also written as ''λ''-calculus) is a formal system in mathematical logic for expressing computation based on function abstraction and application using variable binding and substitution. It is a universal model of computation that can be used to simulate any Turing machine. It was introduced by the mathematician Alonzo Church in the 1930s as part of his research into the foundations of mathematics. Lambda calculus consists of constructing § lambda terms and performing § reduction operations on them. In the simplest form of lambda calculus, terms are built using only the following rules: * x – variable, a character or string representing a parameter or mathematical/logical value. * (\lambda x.M) – abstraction, function definition (M is a lambda term). The variable x becomes bound in the expression. * (M\ N) – application, applying a function M to an argument N. M and N are lambda terms. The reduction operations include: * (\lambda x.M \rightarrow(\l ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Acta Polytechnica Hungarica
Acta or ACTA may refer to: Institutions * Anti-Counterfeiting Trade Agreement, an intellectual property trade agreement * Administrative Council for Terminal Attachments, a standards organization for terminal equipment such as registered jacks * Alameda Corridor Transportation Authority The Alameda Corridor is a freight rail "expressway" owned by the Alameda Corridor Transportation Authority that connects the ports of Los Angeles and Long Beach with the transcontinental mainlines of the BNSF Railway and the Union Pacific Rai ..., in southern California * American Council of Trustees and Alumni, an education organization * Atlantic County Transportation Authority, a transportation agency in Atlantic County, New Jersey * Australian Community Television Alliance, an industry association representing community television licensees in Australia Science and technology * Acta, the transactions (proceedings) of an academic field, a learned society, or an academic conference * Acta ( ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
MAPL
Canadian content (abbreviated CanCon, cancon or can-con; ) refers to the Canadian Radio-television and Telecommunications Commission (CRTC) requirements, derived from the Broadcasting Act of Canada, that radio and television broadcasters (including cable and satellite specialty channels) must produce and/or broadcast a certain percentage of content that was at least partly written, produced, presented, or otherwise contributed to by persons from Canada. CanCon also refers to that content itself, and, more generally, to cultural and creative content that is Canadian in nature. Current Canadian content percentages are as follows: radio airplay is 40% (with partial exceptions for some specialty formats such as classical), and broadcast television is 55% yearly or 50% daily (CBC has a 60% CanCon quota; some specialty or multicultural formats have lower percentages). The loss of the protective Canadian content quota requirements is one of the concerns of those opposed to the Trans ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Dynamic Bayesian Network
A Dynamic Bayesian Network (DBN) is a Bayesian network (BN) which relates variables to each other over adjacent time steps. This is often called a ''Two-Timeslice'' BN (2TBN) because it says that at any point in time T, the value of a variable can be calculated from the internal regressors and the immediate prior value (time T-1). DBNs were developed by Paul Dagum in the early 1990s at Stanford University's Section on Medical Informatics. Dagum developed DBNs to unify and extend traditional linear state-space models such as Kalman filters, linear and normal forecasting models such as ARMA and simple dependency models such as hidden Markov models into a general probabilistic representation and inference mechanism for arbitrary nonlinear and non-normal time-dependent domains. Today, DBNs are common in robotics, and have shown potential for a wide range of data mining applications. For example, they have been used in speech recognition, digital forensics, protein sequencing, an ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Partially Observable Markov Decision Process
A partially observable Markov decision process (POMDP) is a generalization of a Markov decision process (MDP). A POMDP models an agent decision process in which it is assumed that the system dynamics are determined by an MDP, but the agent cannot directly observe the underlying state. Instead, it must maintain a sensor model (the probability distribution of different observations given the underlying state) and the underlying MDP. Unlike the policy function in MDP which maps the underlying states to the actions, POMDP's policy is a mapping from the history of observations (or belief states) to the actions. The POMDP framework is general enough to model a variety of real-world sequential decision processes. Applications include robot navigation problems, machine maintenance, and planning under uncertainty in general. The general framework of Markov decision processes with imperfect information was described by Karl Johan Åström in 1965 in the case of a discrete state space, and it ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
