Automated planning can be used to efficiently recognize goals and plans from partial or full observed action sequences. In this paper, we propose goal recognition heuristics that rely on information from planning landmarks - facts or actions that must occur if a plan is to achieve a goal when starting from some initial state. We develop two such heuristics: the first estimates goal completion by considering the ratio between achieved and extracted landmarks of a candidate goal, while the second takes into account how unique each landmark is among landmarks for all candidate goals. We empirically evaluate these heuristics over both standard goal/plan recognition problems, and a set of very large problems. We show that our heuristics can recognize goals more accurately, and run orders of magnitude faster, than the current state-of-the-art.
Competition Reports102 AI MAGAZINE C omputational models of argumentation are an active research discipline within artificial intelligence that has grown since the beginning of the 1990s (Dung 1995). While still a young field when compared to areas such as SAT solving and logic programming, the argumentation community is very active, with a conference series (COMMA, which began in 2006) and a variety of workshops and special issues of journals. Argumentation has also worked its way into a variety of applications. For example, Williams et al. (2015) described how argumentation techniques are used for recommending cancer treatments, while Toniolo et al. (2015) detail how argumentation-based techniques can support critical thinking and collaborative scientific inquiry or intelligence analysis.Many of the problems that argumentation deals with are computationally difficult, and applications utilizing argumentation therefore require efficient solvers. To encourage this line of research, we organised the First International Competition on Computational Models of Argumentation (ICCMA), with the intention of assessing and promoting state-of-the-art solvers for abstract argumentation problems, and to identify families of challenging benchmarks for such solvers.
The IST-CONTRACT project is in the process of creating an electronic contracting language. One of the goals of this language is that it has formal underpinnings, and formalizations at a number of levels have been created. One of the lowest levels, upon which the other levels are built is the normative level. At this level, we identify how contract clauses (modeled as norms) may evolve over time. In this paper, we describe this formalization, and show how we may associate various states with a norm throughout its lifecycle. We also show how more complex evaluations may be carried out over a norm, and conclude with an example showing the application of the framework over a contract and its associated norms.
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