A Personalized System for Conversational Recommendations

Searching for and making decisions about information is becoming increasingly difficult as the amount of information and number of choices increases. Recommendation systems help users find items of interest of a particular type, such as movies or restaurants, but are still somewhat awkward to use. Our solution is to take advantage of the complementary strengths of personalized recommendation systems and dialogue systems, creating personalized aides. We present a system -- the Adaptive Place Advisor -- that treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. Individual, long-term user preferences are unobtrusively obtained in the course of normal recommendation dialogues and used to direct future conversations with the same user. We present a novel user model that influences both item search and the questions asked during a conversation. We demonstrate the effectiveness of our system in significantly reducing the time and number of interactions required to find a satisfactory item, as compared to a control group of users interacting with a non-adaptive version of the system

A Survey of Knowledge-based Sequential Decision Making under Uncertainty

Reasoning with declarative knowledge (RDK) and sequential decision-making (SDM) are two key research areas in artificial intelligence. RDK methods reason with declarative domain knowledge, including commonsense knowledge, that is either provided a priori or acquired over time, while SDM methods (probabilistic planning and reinforcement learning) seek to compute action policies that maximize the expected cumulative utility over a time horizon; both classes of methods reason in the presence of uncertainty. Despite the rich literature in these two areas, researchers have not fully explored their complementary strengths. In this paper, we survey algorithms that leverage RDK methods while making sequential decisions under uncertainty. We discuss significant developments, open problems, and directions for future work

A canonical theory of dynamic decision-making

Decision-making behavior is studied in many very different fields, from medicine and eco- nomics to psychology and neuroscience, with major contributions from mathematics and statistics, computer science, AI, and other technical disciplines. However the conceptual- ization of what decision-making is and methods for studying it vary greatly and this has resulted in fragmentation of the field. A theory that can accommodate various perspectives may facilitate interdisciplinary working. We present such a theory in which decision-making is articulated as a set of canonical functions that are sufficiently general to accommodate diverse viewpoints, yet sufficiently precise that they can be instantiated in different ways for specific theoretical or practical purposes. The canons cover the whole decision cycle, from the framing of a decision based on the goals, beliefs, and background knowledge of the decision-maker to the formulation of decision options, establishing preferences over them, and making commitments. Commitments can lead to the initiation of new decisions and any step in the cycle can incorporate reasoning about previous decisions and the rationales for them, and lead to revising or abandoning existing commitments. The theory situates decision-making with respect to other high-level cognitive capabilities like problem solving, planning, and collaborative decision-making. The canonical approach is assessed in three domains: cognitive and neuropsychology, artificial intelligence, and decision engineering

Effective Computer Access with Plan-Based Intelligent Screen Reader

The intelligent screen reader system observes a user¡¯s keystrokes when he or she is performing tasks, and infers an underlying plan structure. The system then generates an optimized script that allows the user to perform the same task more efficiently in the future. The intelligent screen reader system is particularly beneficial for visually impaired computer users.The JAWS for Windows screen reader was chosen for this prototype, since the JAWS scripting language provides flexibility to users for customizing their tasks within a specific application. The JAWS macro recorder, developed by Freedom Scientific Inc., records all the user¡¯s actions. Automated Synthesis of Plan Recognition Networks (ASPRN), developed by Intelligent Reasoning Systems Inc., takes a plan or a script representation as input and outputs a specially-constructed belief network that supports plan recognition. The Script Generation Interface, developed by the University of Pittsburgh, allows users to modify the actions recorded by the JAWS macro recorder and generate an optimized script. Development of the SGI involved integrating the SGI with the JAWS macro recorder, integrating the SGI with ASPRN, and implementing the SGI user interface.Preliminary usability testing involving the intelligent screen reader system was conducted with visually impaired users. Within limited actions and tasks, the results are satisfactory. Users felt the SGI was easy to learn and operate, and that it was efficient to create useful scripts with the intelligent screen reader system. Future work includes adding more plans to the plan library and creating a tutorial system

Learning and Reasoning for Robot Dialog and Navigation Tasks

You are viewing an article from the Proceedings of the 21st Annual Meeting of the Special Interest Group on Discourse and Dialogue that was in the Good Systems Network Digest in 2020.Office of the VP for Researc