Near-future electric distribution grids operation will have to rely on demand-side flexibility, both by implementation of demand response strategies and by taking advantage of the intelligent management of increasingly common small-scale energy storage. The Home energy management system (HEMS), installed at low voltage residential clients, will play a crucial role on the flexibility provision to both system operators and market players like aggregators. Modeling and forecasting multi-period flexibility from residential prosumers, such as battery storage and electric water heater, while complying with internal constraints (comfort levels, data privacy) and uncertainty is a complex task. This papers describes a computational method that is capable of efficiently learn and define the feasibility flexibility space from controllable resources connected to a HEMS. An Evolutionary Particle Swarm Optimization (EPSO) algorithm is adopted and reshaped to derive a set of feasible temporal trajectories for the residential net-load, considering storage, flexible appliances, and predefined costumer preferences, as well as load and photovoltaic (PV) forecast uncertainty. A support vector data description (SVDD) algorithm is used to build models capable of classifying feasible and non-feasible HEMS operating trajectories upon request from an optimization/control algorithm operated by a DSO or market player.

We investigate properties of ABA+, a formalism that extends the well studied structured argumentation formalism Assumption-Based Argumentation (ABA) with a preference handling mechanism. In particular, we establish desirable properties that ABA+ semantics exhibit. These pave way to the satisfaction by ABA+ of some (arguably) desirable principles of preference handling in argumentation and nonmonotonic reasoning, as well as non-monotonic inference properties of ABA+ under various semantics.

Team NZ intern Juan Perdomo proved invaluable to the Kiwi America's Cup campaign. Team New Zealand used an artificial intelligence agent to help set up their stunning America's Cup victory. The genius innovations behind the successful Kiwi campaign in Bermuda continue to emerge four months after they blitzed the challenger series and defenders Oracle Team USA to win back the Auld Mug. Stuck in New Zealand and short on money and time to build a second test boat to engage their cycle-powered AC50, the Kiwis went to the computers to find a "virtual" rival to train against. By the time Emirates Team New Zealand lined out against Oracle Team USA for the America's Cup match, helmsman Peter Burling was well schooled in tactics thanks to battling an artificial intelligence agent. The AI was crucial to getting Cup rookie Peter Burling up to speed with starting manoeuvres and tactics in the lightning-fast foiling catamarans.

Artificial intelligence and virtual agents promise improved user experiences and decreased servicing costs. These occur through several pathways. First, virtual agents can be used to ensure the customer is routed to the proper department. Virtual agents are conversational computer programs that interact directly with a customer without human intervention. They are also known as "front end bots", "virtual assistants", or "automated assistants".

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Much research has been done to apply auctions, markets, and negotiation mechanisms to solve the multiagent task allocation problem. However, there has been very little work on human-agent group task allocation. We believe that the notion of bounty hunting has good properties for human-agent group interaction in dynamic task allocation problems. We use previous experimental results comparing bounty hunting with auction-like methods to argue why it would be particularly adept at handling scenarios with unreliable collaborators and unexpectedly hard tasks: scenarios we believe highlight difficulties involved in working with humans collaborators.

In this paper, we demonstrate how a planner (or a robot as an embodiment of it) can explain its decisions to multiple agents in the loop together considering not only the model that it used to come up with its decisions but also the (often misaligned) models of the same task that the other agents might have had. To do this, we build on our previous work on multi-model explanation generation and extend it to account for settings where there is uncertainty of the robot's model of the explainee and/or there are multiple explainees with different models to explain to. We will illustrate these concepts in a demonstration on a robot involved in a typical search and reconnaissance scenario with another human teammate and an external human supervisor.

Both the ethics of autonomous systems and the problems of their technical implementation have by now been studied in some detail. Less attention has been given to the areas in which these two separate concerns meet. This paper, written by both philosophers and engineers of autonomous systems, addresses a number of issues in machine ethics that are located at precisely the intersection between ethics and engineering. We first discuss the main challenges which, in our view, machine ethics posses to moral philosophy. We them consider different approaches towards the conceptual design of autonomous systems and their implications on the ethics implementation in such systems. Then we examine problematic areas regarding the specification and verification of ethical behavior in autonomous systems, particularly with a view towards the requirements of future legislation. We discuss transparency and accountability issues that will be crucial for any future wide deployment of autonomous systems in society. Finally we consider the, often overlooked, possibility of intentional misuse of AI systems and the possible dangers arising out of deliberately unethical design, implementation, and use of autonomous robots.

The ability to act upon a new stimulus, based on previous experience with similar, but distinct, stimuli, sometimes denoted as generalization, is used extensively in our daily life. As a simple example, consider a driver's response to traffic lights: The driver need not recognize the details of a particular traffic light in order to respond to it correctly, even though traffic lights may appear different from one another. The only property that matters is the color, whereas neither shape nor size should play any role in the driver's reaction. Learning how to react to traffic lights thus involves an aspect of generalization. A learning agent, capable of a meaningful and useful generalization is expected to have the following characteristics: (a) an ability for categorization (recognizing that all red signals have a common property, which we can refer to as redness); (b) an ability to classify (a new red object is to be related to the group of objects with the redness property); (c) ideally, only generalizations that are relevant for the success of the agent should be learned (red signals should be treated the same, whereas squareshaped signals should not, as they share no property that is of relevance in this context); (d) correct actions should be associated with relevant generalized properties (the driver should stop whenever a red signal is shown); and (e) the generalization mechanism should be flexible. To illustrate what we mean by "flexible generalization", let us go back to our driver. After learning how to handle traffic lights correctly, the driver tries to follow arrow signs to, say, a nearby airport. Clearly, it is now the shape category of the signal that should guide the driver, rather than the color category.

In this paper we consider distributed optimization problems over a multi-agent network, where each agent can only partially evaluate the objective function, and it is allowed to exchange messages with its immediate neighbors. Differently from all existing works on distributed optimization, our focus is given to optimizing a class of difficult non-convex problems, and under the challenging setting where each agent can only access the zeroth-order information (i.e., the functional values) of its local functions. For different types of network topologies such as undirected connected networks or star networks, we develop efficient distributed algorithms and rigorously analyze their convergence and rate of convergence (to the set of stationary solutions). Numerical results are provided to demonstrate the efficiency of the proposed algorithms.