We present an approach to reasoning with strict and defeasible rules over literals. A controlled natural language is employed as human/machine interface to facilitate the specification of knowledge and verbalization of results. Reasoning on the rules is done by a direct semantics that addresses several issues for current approaches to argumentation-based defeasible reasoning. Techniques from formal argumentation theory are employed to justify conclusions of the approach; therefore, we not only address automated reasoning but also human acceptance of provided conclusions.

When acting, agents may deviate from the optimal plan, either because they are not perfect optimizers or because they interleave multiple unrelated tasks. In this paper, we detect such deviations by analyzing a set of observations and a monitored goal to determine if an observed agent's actions contribute towards achieving the goal. We address this problem without pre-defined static plan libraries, and instead use a planning domain definition to represent the problem and the expected agent behavior. At the core of our approach, we exploit domain-independent heuristics for estimating the goal distance, incorporating the concept of landmarks (actions which all plans must undertake if they are to achieve the goal). We evaluate the resulting approach empirically using several known planning domains, and demonstrate that our approach effectively detects such deviations.

We demonstrate an implementation of Markov Argumentation Random Fields (MARFs), a novel formalism combining elements of formal argumentation theory and probabilistic graphical models. In doing so MARFs provide a principled technique for the merger of probabilistic graphical models and non-monotonic reasoning, supporting human reasoning in ``messy’’ domains where the knowledge about conflicts should be applied. Our implementation takes the form of a graphical tool which supports users in interpreting complex information. We have evaluated our implementation in the domain of intelligence analysis, where analysts must reason and determine likelihoods of events using information obtained from conflicting sources.

We review the First International Competition on Computational Models of Argumentation (ICMMA'15). The competition evaluated submitted solvers performance on four different computational tasks related to solving abstract argumentation frameworks. Each task evaluated solvers in ways that pushed the edge of existing performance by introducing new challenges. Despite being the first competition in this area, the high number of competitors entered, and differences in results, suggest that the competition will help shape the landscape of ongoing developments in argumentation theory solvers.

We review the First International Competition on Computational Models of Argumentation (ICMMA’15). The competition evaluated submitted solvers performance on four different computational tasks related to solving abstract argumentation frameworks. Each task evaluated solvers in ways that pushed the edge of existing performance by introducing new challenges. Despite being the first competition in this area, the high number of competitors entered, and differences in results, suggest that the competition will help shape the landscape of ongoing developments in argumentation theory solvers.

The AAAI 2014 Fall Symposium Series was held Thursday through Saturday, November 13–15, at the Westin Arlington Gateway in Arlington, Virginia adjacent to Washington, DC. The titles of the seven symposia were Artificial Intelligence for Human-Robot Interaction, Energy Market Prediction, Expanding the Boundaries of Health Informatics Using AI, Knowledge, Skill, and Behavior Transfer in Autonomous Robots, Modeling Changing Perspectives: Reconceptualizing Sensorimotor Experiences, Natural Language Access to Big Data, and The Nature of Humans and Machines: A Multidisciplinary Discourse. The highlights of each symposium are presented in this report.

The program also included six keynote presentations, a funding panel, a community panel, and multiple breakout sessions. The keynote presentations, given by speakers that have been working on AI for HRI for many years, focused on the larger intellectual picture of this subfield. Each speaker was asked to address, from his or her personal perspective, why HRI is an AI problem and how AI research can bring us closer to the reality of humans interacting with robots on everyday tasks. Speakers included Rodney Brooks (Rethink Robotics), Manuela Veloso (Carnegie Mellon University), Michael Goodrich (Brigham Young University), Benjamin Kuipers (University of Michigan), Maja Mataric (University of Southern California), and Brian Scassellati (Yale University).

We describe a tool, MatchMaps, that generates sameAs and partOf matches between spatial objects (such as shops, shopping centres, etc.) in crowd-sourced and authoritative geospatial datasets. MatchMaps uses reasoning in qualitative spatial logic, description logic and truth maintenance techniques, to produce a consistent set of matches. We report the results of an initial evaluation of MatchMaps by experts from Ordnance Survey (Great Britain's National Mapping Authority). In both the case studies considered, MatchMaps was able to correctly match spatial objects (high precision and recall) with minimal human intervention.

Abstract argumentation framework ( AF ) is a unifying framework able to encompass a variety of nonmonotonic reasoning approaches, logic programming and computational argumentation. Yet, efficient approaches for most of the decision and enumeration problems associated to AF s are missing, thus limiting the efficacy of argumentation-based approaches in real domains. In this paper, we present an algorithm for enumerating the preferred extensions of abstract argumentation frameworks which exploits parallel computation. To this purpose, the SCC-recursive semantics definition schema is adopted, where extensions are defined at the level of specific sub-frameworks. The algorithm shows significant performance improvements in large frameworks, in terms of number of solutions found and speedup.

We are interested in the manipulation skills required by future service robots performing everyday tasks such as preparing food and cleaning in a typical home environment. Such robots must have a robust set of skills that can be applied in the unpredictable and varying circumstances that arise in everyday life.To succeed in such a setting, a service robot must have a strong ability to transfer old skills to new varied settings. We are inspired by the strong transfer ability demonstrated by infants and toddlers on simple manipulation activities, and we are motivated to try and replicate these abilities in an artificial system.We treat this as a problem of making analogies, and describe a theoretical framework which could account for it. We sketch the ideas of a computational model for implementing the required analogical reasoning.