This paper presents an ontology-based application for mobile devices which is responsible for supporting groups of people with the management of their shared tasks. The ontology stores the domain knowledge about collaborative tasks, which is used to support task recognition and relocation. Such knowledge is used by a multi-agent system that consists of a group of agents representing each person in the group. The agents use plan recognition techniques to monitor the execution of tasks according to the schedules and negotiate task allocation when needed. Our techniques have been applied in a healthcare scenario which consists of a family group that takes care of an elderly person. This paper presents an ontology-based application for mobile devices which is responsible for supporting groups of people with the management of their shared tasks. % in a healthcare scenario.The ontology stores the domain knowledge about collaborative tasks, which is used to support task recognition and relocation.Such knowledge is used by a multi-agent system that consists of a group of agents representing each person in the group.The agents use plan recognition techniques to monitor the execution of tasks according to the schedules and negotiate task allocation when needed.Our techniques have been applied in a healthcare scenario which consists of a family group that takes care of an elderly person.

While domain descriptions are often shared and manipulated through diagrams, most complex domains are still described using text-based languages. Code becomes an intermediary between the real-world and an abstract idea, and the programmer is merely a converter of diagrams into code. For automated planning this is no different. The state transition function is described in terms of a textual representation of actions and, although simple actions require little effort to define by the user, the learning process is often slow. New users have no metaphor to help them to visualize the domain description that they are working on and little information about why a planner fails due to formalization errors. In this paper, we propose a visual abstraction for both the planning domain actions and the planning process itself, to facilitate the design of classical planning domains. Using this abstraction, we expect to improve the learning curve for defining and subsequently diagnosing problems with new planning domains.

Planning as programming is an approach to automated planning, where the planning domain model is expressed as a program in some (declarative) programming language. Then the modeler can exploit all features of that language to encode control knowledge important for efficient planning. In this paper we study these features in the logic programming language Picat and its planner module. In particular, we use two planning benchmarks, Nomystery and Childsnack, to compare factored and structured representations of states extended by encodings of control knowledge.

Google wants to take some of the pain out of scheduling work meetings with a new feature the company launched for its Calendar product on Wednesday. The new "Find a Time" feature in the Google Calendar Android app helps users pick out a time that works for everyone invited to a meeting without requiring them to spend ages going back and forth over email. Here's how it works: when a user sets up a meeting and adds people to the event, Find a Time will pick out a list of suggested times, along with who will be able to attend. Those suggestions will be built not only on the current state of an invitee's calendar, but also their historical scheduling trends. Once the organizer has picked a time, Google Calendar will send out invitations to everyone.

It's one thing to keep robots from crashing into fixed obstacles like walls or furniture, but preventing collisions with other moving things is a much tougher challenge. Targeting teams of robots working together, MIT on Thursday announced a new algorithm that helps robots avoid moving objects. Planning algorithms for robot teams can be centralized, in which a single computer makes decisions for the whole team, or decentralized, in which each robot makes its own decisions. The latter approach is much better in terms of incorporating local observations, but it's also much trickier, since each robot must essentially guess what the others are going to do. MIT's new algorithm takes a decentralized approach and factors in not just stationary obstacles but also moving ones.

Application of UAV in indoor environment is emerging nowadays due to the advancements in technology. UAV brings more space-flexibility in an occupied or hardly-accessible indoor environment, e.g., shop floor of manufacturing industry, greenhouse, nuclear powerplant. UAV helps in creating an autonomous manufacturing system by executing tasks with less human intervention in time-efficient manner. Consequently, a scheduler is one essential component to be focused on; yet the number of reported studies on UAV scheduling has been minimal. This work proposes a methodology with a heuristic (based on Earliest Available Time algorithm) which assigns tasks to UAVs with an objective of minimizing the makespan. In addition, a quick response towards uncertain events and a quick creation of new high-quality feasible schedule are needed. Hence, the proposed heuristic is incorporated with Particle Swarm Optimization (PSO) algorithm to find a quick near optimal schedule. This proposed methodology is implemented into a scheduler and tested on a few scales of datasets generated based on a real flight demonstration. Performance evaluation of scheduler is discussed in detail and the best solution obtained from a selected set of parameters is reported.

With renewable energy becoming more common, energy prices fluctuate more depending on environmental factors such as the weather. Consuming energy without taking volatile prices into consideration can not only become expensive, but may also increase the peak load, which requires energy providers to generate additional energy using less environment-friendly methods. In the Netherlands, pumping stations that maintain the water levels of polder canals are large energy consumers, but the controller software currently used in the industry does not take real-time energy availability into account. We investigate if existing AI planning techniques have the potential to improve upon the current solutions. In particular, we propose a light weight but realistic simulator and investigate if an online planning method (UCT) can utilise this simulator to improve the cost-efficiency of pumping station control policies. An empirical comparison with the current control algorithms indicates that substantial cost, and thus peak load, reduction can be attained.

We introduce Scheduling MTL (SMTL) an extension of Metric Temporal Logic that supports the specification of complex scheduling problems with repeated and conditional occurrences of activities, and rich temporal relationships among them. We define the syntax and semantics of SMTL, and explore natural restrictions of the language to gain tractability. We also provide an algorithm for finding a schedule to a problem specified as an SMTL formula, and establish a novel equivalence between a fragment of MTL and simple temporal networks, a widely-used formalism in AI temporal planning.

Domain-independent planning requires only to specify planning problems in a standard language (e.g. PDDL) in order to utilise planning in some application. Despite a huge advancement in domain-independent planning, some relatively-easy problems are still challenging for existing planning engines. Such an issue can be mitigated by specifying Domain Control Knowledge (DCK) that can provide better guidance for planning engines. In this paper, we introduce transition-based DCK, inspired by Finite State Automata, that is efficient as demonstrated empirically, planner-independent (can be encoded within planning problems) and easy to specify.

Generalized plans, such as plans with loops, are widely used in AI. Among other things, they are straightforward to execute, they allow action repetition, and they solve multiple problem instances. However, the correctness of such plans is non-trivial to define, making it difficult to provide a clear specification of what we should be looking for. Proposals in the literature, such as strong planning, are universally adopted by the community, but were initially formulated for finite state systems. There is yet to emerge a study on the sensitivity of such correctness notions to the structural assumptions of the underlying plan framework. In this paper, we are interested in the applicability and correctness of generalized plans in domains that are possibly unbounded, and/or stochastic, and/or continuous. To that end, we introduce a generic controller framework to capture different types of planning domains. Using this framework, we then study a number of termination and goal satisfaction criteria from first principles, relate them to existing proposals, and show plans that meet these criteria in the different types of domains.