This paper details how OntoAgents, language-endowed intelligent agents developed in the OntoAgent framework, assess their confidence in understanding language inputs. It presents scoring heuristics for the following subtasks of natural language understanding: lexical disambiguation and the establishment of semantic dependencies; reference resolution; nominal compounding; the treatment of fragments; and the interpretation of indirect speech acts. The scoring of confidence in individual linguistic subtasks is a prerequisite for computing the overall confidence in the understanding of an utterance. This, in turn, is a prerequisite for the agent’s deciding how to act upon that level of understanding.

Timely, relevant, and accurate intelligence analysis is critical to national security, but it is astonishingly complex. This paper provides an intuitive overview of Cogent, a cognitive assistant that facilitates a synergistic integration of analyst's imaginative reasoning with agent's critical reasoning to draw defensible and persuasive conclusions from masses of evidence, in a world that is changing all the time. It presents Cogent's design goals characterizing a new generation of structured analytical tools, introduces the evidence-based analysis concepts on which it is grounded, illustrates a sample session with its current version, and summarizes the cognitive assistance provided to its user.

This paper analyzes repeated multimarket contact with observation errors where two players operate in multiple markets simultaneously. Multimarket contact has received much attention from the literature of economics,management, and information systems. Despite vast empirical studies that examine whether multimarket contact fosters cooperation/collusion, little is theoretically known as to how players behave in an equilibrium when each player receives a noisy observation of other firms’ actions. This paper tackles an essentially realistic situation where the players do not share common information; each player may observe a different signal (private monitoring). Thus, players have difficulty in having a common understanding about which market their opponent should be punished in and when punishment should be started and ended. We first theoretically show that an extension of 1-period mutual punishment (1MP) for an arbitrary number of markets can be an equilibrium. Second, by applying a verification method, we identify a simple equilibrium strategy called "locally cautioning (LC)" that restores collusion after observation error or deviation. We then numerically reveal that LC significantly outperforms 1MP and achieves the highest degree of collusion.

Believability requires video game characters to consider their actions within the context of social norms. Social norms involve a broad range of behavioral defaults, obligations, and injunctions unrelated to strictly causal reasoning.  Defeasible reasoning involves rationally compelling but deductively invalid arguments, such as reasoning with rules that allow exceptions. This paper investigates having video game characters use defeasible reasoning to consider injunctive social norms when selecting and planning actions.

Displaying believable emotional reactions in virtual characters is required in applications ranging from virtual-reality trainers to video games. Manual scripting is the most frequently used method and enables an arbitrarily high fidelity of the emotions displayed. However, scripting is labour intense and greatly reduces the scope of emotions displayed and emotionally affected behavior in virtual characters. As a result, only a few virtual characters can display believable emotions and only in pre-scripted encounters. In this paper we implement and evaluate a lightweight algorithm for procedurally controlling both emotionally affected behavior and emotional appearance of a virtual character. The algorithm is based on two psychological models of emotions: conservation of resources and appraisal. The former component controls emotionally affected behavior of a virtual character whereas the latter generates explicit numeric descriptors of the character's emotions which can be used to drive the character's appearance. We implement the algorithm in a simple testbed and compare it to two baseline approaches via a user study. Human participants judged the emotions displayed by the algorithm to be more believable than those of the baselines.

Artificial Intelligence (AI) techniques have been widely used in video games to control non-playable characters. More recently, AI has been applied to automated story generation with the objective of managing the player's experience in an interactive narrative. Such AI experience managers can generate and adapt narrative dynamically, often in response to the player's in-game actions. We implement and evaluate a recently proposed AI experience manager, PACE, which predicts the player's emotional response to a narrative event and uses such predictions to shape the narrative to keep the player on an author-supplied target emotional curve.

Reducing energy consumption of climate control systems is important in order to reduce human environmental footprint. The need to save energy becomes even greater when considering an electric car, since heavy use of the climate control system may exhaust the battery. In this article we consider a method for an automated agent to provide advice to drivers which will motivate them to reduce the energy consumption of their climate control unit. Our approach takes into account both the energy consumption of the climate control system and the expected comfort level of the driver. We therefore build two models, one for assessing the energy consumption of the climate control system as a function of the system’s settings, and the other, models human comfort level as a function of the climate control system’s settings. Using these models, the agent provides advice to the driver considering how to set the climate control system. The agent advises settings which try to preserve a high level of comfort while consuming as little energy as possible. We empirically show that drivers equipped with our agent which provides them with advice significantly save energy as compared to drivers not equipped with our agent.

The AAAI-15 organizing committee of about 60 researchers arranged many of the traditional AAAI events, including the Innovative Applications of Artificial Intelligence (IAAI) Conference, tutorials, workshops, the video competition, senior member summary talks (on well-developed bodies of research or important new research areas), and What's Hot talks (on research trends observed in other AIrelated conferences and, for the first time, competitions). Innovations of AAAI-15 included software and hardware demonstration programs, a virtual agent exhibition, a computer-game showcase, a funding information session with program directors from different funding agencies, and Blue Sky Idea talks (on visions intended to stimulate new directions in AI research) with awards funded by the CRA Computing Community Consortium. Seven invited talks surveyed AI research in academia and industry and its impact on society. Attendees kept track of the program through a smartphone app as well as social media channels.

The AAAI 2015 Spring Symposium Series was held Monday through Wednesday, March 23-25, at Stanford University near Palo Alto, California. The titles of the seven symposia were Ambient Intelligence for Health and Cognitive Enhancement, Applied Computational Game Theory, Foundations of Autonomy and Its (Cyber) Threats: From Individuals to Interdependence, Knowledge Representation and Reasoning: Integrating Symbolic and Neural Approaches, Logical Formalizations of Commonsense Reasoning, Socio-Technical Behavior Mining: From Data to Decisions, Structured Data for Humanitarian Technologies: Perfect Fit or Overkill? and Turn-Taking and Coordination in Human-Machine Interaction.The highlights of each symposium are presented in this report.

In real-time domains such as video games, planning happens concurrently with execution and the planning algorithm has a strictly bounded amount of time before it must return the next action for the agent to execute. We explore the use of real-time heuristic search in two benchmark domains inspired by video games. Unlike classic benchmarks such as grid pathfinding and the sliding tile puzzle, these new domains feature exogenous change and directed state space graphs. We consider the setting in which planning and acting are concurrent and we use the natural objective of minimizing goal achievement time. Using both the classic benchmarks and the new domains, we investigate several enhancements to a leading real-time search algorithm, LSS-LRTA*. We show experimentally that 1) it is better to plan after each action or to use a dynamically sized lookahead, 2) A*-based lookahead can cause undesirable actions to be selected, and 3) on-line de-biasing of the heuristic can lead to improved performance. We hope this work encourages future research on applying real-time search in dynamic domains.