The AAAI-14 Workshop program was held Sunday and Monday, July 27–28, 2012, at the Québec City Convention Centre in Québec, Canada. Canada. The AAAI-14 workshop program included fifteen workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were AI and Robotics; Artificial Intelligence Applied to Assistive Technologies and Smart Environments; Cognitive Computing for Augmented Human Intelligence; Computer Poker and Imperfect Information; Discovery Informatics; Incentives and Trust in Electronic Communities; Intelligent Cinematography and Editing; Machine Learning for Interactive Systems: Bridging the Gap between Perception, Action and Communication; Modern Artificial Intelligence for Health Analytics; Multiagent Interaction without Prior Coordination; Multidisciplinary Workshop on Advances in Preference Handling; Semantic Cities — Beyond Open Data to Models, Standards and Reasoning; Sequential Decision Making with Big Data; Statistical Relational AI; and The World Wide Web and Public Health Intelligence. This article presents short summaries of those events.

Actions in a multi-agent setting have complex characteristics. They may not only affect the real world, but also affect the knowledge and beliefs of agents in the world. In many cases, the effect on the beliefs or knowledge of an agent is not due to that agent actively doing some actions, but could be simply the result of that agent’s perspective in terms of where it is looking. In dynamic epistemic logic (DEL), such multi-agent actions are expressed as complex constructs or as Kripke model type structures. This paper uses the multi-agent action language mA+ to show how one can take advantage of some of the perspective fluents of the world to model com- plex actions, in the sense of DEL, as simple action sequences. The paper describes several plan modules using such actions. Such plan modules will be helpful in planning for belief and knowledge goals in a multi-agent setting, as planning from scratch would often be prohibitively time consuming.

We present an emerging research project in our laboratory to extend ambient intelligence (AmI) by what we refer to as “extreme personalization” meaning that an instance of ambient intelligence is focused on one or at most a few individuals over a very long period of time. Over a lifetime of co-activity, it senses and adapts to a person’s preferences and experiences, and crucially, his or her (changing) special needs; needs that differ significantly from the normal baseline. We refer to our agent-based cyber-physical system as Ambient Personal Environment eXperiment (APEX). It aims to serve as a Companion , a Coach , and a Caregiver : crucial support for individuals with mental, physical, and age-related disabilities and those other people who help them. We propose that an instance of APEX, interacting socially with each of these people, is both a social actor as well as a cyber-human prosthetic device . APEX is an ambitious integration of multiple technologies from Artificial Intelligence (AI) and other disciplines. Its successful development can be viewed as a grand challenge for AI. We discuss in this paper three research thrusts that lead toward our vision:  robust intelligent agents, semantically rich human-machine interaction, and reasoning from comprehensive multi-modal behavior data.

The aim of this paper is to define a both pragmatic and formal method allowing a social or technical entity to define its own strategic goals and plans. Indeed, by definition, an autonomous entity ought to be governed only by its own principles and laws. Thus, the core concept of autonomy is the capability of defining this principles regarding its own objectives and plans. Thus, the robustness of any autonomous system relies on the pivotal concept of Self-Orientation. This paper focuses on the first formal steps of Self-Orientation theories for any group of agents.

We obtain the conditions for the emergence of the swarm intelligence effect in an interactive game of restless multi-armed bandit (rMAB). A player competes with multiple agents. Each bandit has a payoff that changes with a probability $p_{c}$ per round. The agents and player choose one of three options: (1) Exploit (a good bandit), (2) Innovate (asocial learning for a good bandit among $n_{I}$ randomly chosen bandits), and (3) Observe (social learning for a good bandit). Each agent has two parameters $(c,p_{obs})$ to specify the decision: (i) $c$, the threshold value for Exploit, and (ii) $p_{obs}$, the probability for Observe in learning. The parameters $(c,p_{obs})$ are uniformly distributed. We determine the optimal strategies for the player using complete knowledge about the rMAB. We show whether or not social or asocial learning is more optimal in the $(p_{c},n_{I})$ space and define the swarm intelligence effect. We conduct a laboratory experiment (67 subjects) and observe the swarm intelligence effect only if $(p_{c},n_{I})$ are chosen so that social learning is far more optimal than asocial learning.

Distributed estimation, detection, and learning theory in networks have attracted much attention over the past decades [1], [2], [3], [4], with applications that range from sensor and robotic networks [5], [6], [7], [8], [9] to social and economic networks [10], [11], [12]. In these scenarios, agents in a network need to learn the value of a parameter that they may not be able to infer on their own, but the global spread of information in the network provides them with adequate data to learn the truth collectively. As a result, agents iteratively exchange information with their neighbors. For instance, in distributed sensor and robotic networks, agents use local diffusion to augment their imperfect observations with information from their neighbors and achieve consensus and coordination [13], [14]. Similarly, agents exchange beliefs in social networks to benefit from each other's observations and private information and learn the unknown state of the world [15], [16]. Existing literature on distributed learning focuses mostly on environments where individuals communicate at every round. Of particular relevance to our discussion are a host of algorithms that follow the non-Bayesian learning scheme in Jadbabaie et.

The number of people with disabilities is continuously increasing. Providing patients who have disabilities with the rehabilitation and care necessary to allow them good quality of life creates overwhelming demands for health and rehabilitation services. We suggest that advancements in intelligent agent technology provide new opportunities for improving the provided services. We will discuss the challenges of building an agent for the health care domain and present four capabilities that are required for an agent in the health care domain: planning, monitoring, intervention and encouragement. We will discuss the importance of personalizing all of them and the needto facilitate cooperation between the automated agent and the human care givers. We will review recent technology that can be used toward the development of agents that can have these capabilities and their promise in automating services such as physiotherapy, speech therapy and cognitive training.

Commitment protocols provide an effective formalism for the regulation of agent interaction. Although existing work mainly focus on the design-time development of static commitment protocols, recent studies propose methods to create them dynamically at run-time with respect to the goals of the agents. These methods require agents to verify new commitment protocols taking their goals, and beliefs about the other agents’ behavior into account. Accordingly, in this paper, we first propose a probabilistic model to formally capture commitment protocols according to agents’ beliefs. Secondly, we identify a set of important properties for the verification of a new commitment protocol from an agent’s perspective and formalize these properties in our model. Thirdly, we develop probabilistic model checking algorithms with advanced reduction for efficient verification of these properties. Finally, we implement these algorithms as a tool and evaluate the proposed properties over different commitment protocols.

We present the SimSensei system, a fully automatic virtual agent that conducts interviews to assess indicators of psychological distress. We emphasize on the perception part of the system, a multimodal framework which captures and analyzes user state for both behavioral understanding and interactional purposes.

The multi-agent path finding (MAPF) problem is a generalization of the single-agent path finding problem for k > 1 agents. It consists of a graph and a number of agents. Foreach agent, a unique start state and a unique goal state are given, the task is to find paths for all agents from their start states to their goal states, under the constraint that agents cannot collide during their movements. In many cases there is an additional goal of minimizing a cumulative cost function such as the sum of the time steps required for every agent to reach its goal. The goal of my research is providing new methods to solve MAPF optimally and provide theoretical understandings that will help choose the best solver given a problem instance.