Video games are complex simulation environments with many real-world properties that need to be addressed in order to build robust intelligence. In particular, real-time strategy games provide a multi-scale challenge which requires both deliberative and reactive reasoning processes. Experts approach this task by studying a corpus of games, building models for anticipating opponent actions, and practicing within the game environment. We motivate the need for integrating heterogeneous approaches by enumerating a range of competencies involved in gameplay and discuss how they are being implemented in EISBot, a reactive planning agent that we have applied to the task of playing real-time strategy games at the same granularity as humans.

In this paper, we address the problem of communicating, interpreting,and executing complex yet abstract instructions to a robot teammember. This requires specifying the tasks in an unambiguous manner,translating them into operational procedures, and carrying outthose procedures in a persistent yet reactive manner. We reportour response to these issues, after which we demonstrate theircombined use in controlling a mobile robot in a multi-room officesetting on tasks similar to those in search-and-rescue operations.We conclude by discussing related research and suggesting directionsfor future work.

Inference techniques play a central role in many cognitive systems. They transform low-level observations of the environment into high-level, actionable knowledge which then gets used by mechanisms that drive action, problem-solving, and learning. This paper presents an initial effort at combining results from AI and psychology into a pragmatic and scalable computational reasoning system. Our approach combines a numeric notion of plausibility with first-order logic to produce an incremental inference engine that is guided by heuristics derived from the psychological literature. We illustrate core ideas with detailed examples and discuss the advantages of the approach with respect to cognitive systems.

This paper proposes a novel agenda for cognitive systems research focused on the "social agency" problem, which concerns acting to produce mental states in other agents in addition to physical states of the world. The capacity for social agency will enable agents to perform a wide array of tasks in close association with people and is a valuable first step towards broader social cognition. We argue that existing cognitive systems have not addressed social agency because they lack a number of the required mechanisms. We describe an initial approach set in a toy scenario based on capabilities native to the ICARUS cognitive architecture. We utilize an analysis of this approach to highlight the open issues required for social agency and to encourage other researchers to address this important problem.

The Betty’s Brain computer-based learning system provides an open-ended and choice-rich environment for science learning. Using the learning-by-teaching paradigm paired with feedback and support provided by two pedagogical agents, the system also promotes the development of self-regulated learning strategies to support preparation for future learning. We apply metacognitive learning theories and experiential analysis to interpret the results from previous classroom studies. We propose an integrated cognitive and metacognitive model for effective, self-regulated student learning in the Betty’s Brain environment, and then apply this model to interpret and analyze common suboptimal learning strategies students apply during their learning. This comparison is used to derive feedback for helping learners overcome these difficulties and adopt more effective strategies for regulating their learning. Preliminary results demonstrate that students who were responsive to the feedback had better learning performance.

Our approach to mind-design derives from the view of language as a mirror of mind — a view compatible with the linguistic orientation of the Turing Test, and more concretely, with the remarkably tight coupling between linguistic structure and semantic entailment demonstrated by Richard Montague. Additional evidence for the power of this perspective comes from recent work in Natural Logic (NLog), in a sense a method of "reading off" certain obvious inferences directly from linguistic structure. Thus much of our past emphasis has been on developing a knowledge representation, Episodic Logic (EL), matching the expressivity of language, and inference machinery for this representation. More recently we have been striving to create broad bases of general world knowledge and lexical knowledge, while also adapting the latest version of our EPILOG inference engine to the kinds of obvious inferences that are the forte of NLog. At this point our knowledge collections range from sets of a few dozen core lexical axioms to millions of general "factoids" and quantified axioms derived from many of these, all expressed in EL. At the same time we have shown that EPILOG easily handles NLog-like inferences as well as ones beyond the scope of NLog.

Cognitive systems with human-level intelligence must dis­play a wide range of abilities, including reasoning about the beliefs of others, hypothetical and future situations, quanti­fiers, probabilities, and counterfactuals. While each of these deals in some way with reasoning about alternative states of reality, no single knowledge representation framework deals with them in a unified and scalable manner. As a conse­quence it is difficult to build cognitive systems for domains that require each of these abilities to be used together. To enable this integration we propose a representational framework based on synchronizing beliefs between worlds. Using this framework, each of these tasks can be reformu­lated into a reasoning problem involving worlds. This demonstrates that the notions of worlds and inheritance can bring significant parsimony and broad new abilities to knowledge representation.

This article proposes a suite of design decisions for the overall design of an Artificial Intelligence, i.e., a software system that exhibits intelligence in the spirit of the early days of A.I. research. The key aspects of the proposal are: (1) The identification of the A.I. system as a software individual that has the properties of integrity and persistence; (2) The construction of a software platform that integrates aspects of incremental programming languages and systems as well as of operating systems, with aspects that are intrinsic to knowledge-based artificial intelligence; (3) The use of a representation language that builds on essential aspects of S-expressions, Lisp, logic and extended set theory, but which is used both as a vehicle for software and as a publication language e.g. in lecture notes; (4) The identification of actions and aggregates of actions as first-class citizens in the representation language and as an important type of data object in the software system. The article also describes the Leonardo software platform, its representation language, its educational resources and its knowledgebase library which is one implementation of these proposed design decisions. Finally it makes a proposal concerning the research paradigm for this research area.

Desiderata for cognitive architectures that are to support the extent of human-level intelligence required in virtual humans imply the need to bridge a range of dichotomies faced by such architectures. The focus here is first on two general approaches to building such bridges — addition and reduction — and then on a pair of general tools – graphical models and piecewise continuous functions — that exploit the second approach towards developing such an architecture. Evaluation is in terms of the architecture’s demonstrated ability and future potential for bridging the dichotomies.

Historically, approaches to human-level intelligence have divided between those emphasizing the mechanisms involved, such as cognitive architectures, and those focusing on the knowledge content, such as ontologies. In this paper we argue that in order to build cognitive systems capable of human-level event-recognition, a comprehensive infrastructure of perceptual and cognitive mechanisms coupled with high-level knowledge representations is required. In particular, our contribution focuses on an integrated modeling framework (the “Cognitive Engine”), where the learning and knowledge retrieval mechanisms of the ACT-R cognitive architecture are combined with integrated semantic resources for the purpose of event interpretation.